Targeting NRAS Inhibits Cancer Cell Growth and Enhances Paclitaxel Sensitivity in Lung Adenocarcinoma

Targeting NRAS Inhibits Cancer Cell Growth and Enhances Paclitaxel Sensitivity 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 Targeting NRAS Inhibits Cancer Cell Growth and Enhances Paclitaxel Sensitivity in Lung Adenocarcinoma Taoming Mo, Shuang Zhang, Qishuang Wei, Yali Zhang, Li Tong, Sichu Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4306414/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung adenocarcinoma (LUAD)poses substantial therapeutic complexities due to its resistance to first-line chemotherapic agents such as paclitaxel. This study investigated the involvement of NRAS in the development of paclitaxel resistance in LUAD. We integrated transcriptome data from TCGA and other databases while conducting in vitro experiments We also used GSEA to identify NRAS-related genes and pathways. Our findings revealed a significant up-regulation of NRAS in LUAD tissue, with higher NRAS expression correlating with adverse patient outcomes and decreased sensitivity to paclitaxel. Pathway enrichment analysis further revealed that NRAS-related genes significantly contributed to cell cycle dysregulation and impaired DNA damage repair mechanisms. Additionally, our experiments demonstrated that NRAS knockdown in LUAD cell lines exhibited increased sensitivity to paclitaxel, suggesting its potential as a viable therapeutic target. Targeting NRAS has the potential to enhance the efficacy of paclitaxel treatment in LUAD patients, offering a hopeful avenue for enhancing patient prognosis. NRAS Paclitaxel Chemotherapy Sensitivity LUAD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Lung cancer stands as the leading cause of cancer-related mortality, responsible for approximately 1.8 million deaths, representing 18% of all cancer-related fatalities[1, 2]. Non-small cell lung cancer (NSCLC) accounts for about 85% of newly diagnosed lung cancer cases, posing a significant challenge with a 5-year survival rate of less than 20%[3, 4]. Among NSCLC subtypes, lung adenocarcinoma (LUAD) has emerged as the most prevalent histological subtype, with an increasing incidence observed among young women and individuals who have never smoked. Alarmingly, nearly 70% of patients receive their diagnosis at an advanced disease stage. Current treatment modalities encompass a range of approaches, including surgical interventions, chemotherapy, and radiotherapy. Despite advancements in surgical techniques, the 5-year survival rate for surgically resectable NSCLC patients has seen only marginal improvements in recent decades[5]. In the face of progress in targeted therapies and immunotherapy, chemotherapy remains the cornerstone of treatment for advanced lung adenocarcinoma[6]. Paclitaxel holds an important role as a first-line chemotherapy drug. However, clinical observations indicated an increasing resistance to paclitaxel in recent years, necessitating an urgent search for targets that could increase the sensitivity of LUAD cells to this drug. Paclitaxel plays a pivotal role as a first-line chemotherapy drug in treating non-small-cell lung cancer (NSCLC) and various other malignancies. Its mechanism of action involves acting as a microtubule stabilizer, exerting anti-cancer effects by preventing microtubule depolymerization. This reduces microtubule dynamics within the mitotic spindle, leading to G2/M cell cycle arrest and initiating apoptosis, ultimately impeding tumor cell mitosis and growth[7, 8]. A notable aspect of paclitaxel is its induction of α-tubulin acetylation, resulting in the acetylated α-tubulin’s localization within the microtubule organization center (MTOC). This action potentially alters microtubule dynamics[9]. However, in the context of advanced LUAD, the efficacy of paclitaxel is often compromised by the emergence of drug resistance mechanisms. These mechanisms include the overexpression of cellular pumps, such as P-glycoprotein[10], microtubule dynamics alterations[11], and apoptotic pathways modifications[12]. Such changes can diminish cell responsiveness to drug-induced death signals, necessitating the exploration of novel therapeutic avenues. While some studies have investigated the potential synergistic effects of combining paclitaxel with other drugs to enhance its efficacy, the 5-year survival rate for LUAD patients remains disappointingly below 20%. Consequently, a pressing need exists to identify targets that could increase the sensitivity of LUAD cells to paclitaxel. RAS genes stand out as the most frequently mutated oncogenes in cancer. These genes wield significant power within the RAS pathway, assuming a central role in various biological processes[13]. These include cell growth, proliferation, differentiation, migration, and survival. The dysregulated activity of this pathway is a hallmark of cancer, often leading to uncontrolled cell proliferation. The relationship between paclitaxel resistance and the RAS pathway has been reported in previous studies including bladder and colorectal[14, 15]. However, the association has been relatively unexplored in lung adenocarcinoma. RAS oncogenes encode a family of membrane-associated proteins critical in regulating signal transduction upon binding to various membrane receptors. Among these, NRAS, KRAS, and HRAS form the core of the RAS gene family. NRAS, an isomeric family member, functions as a small G protein transitioning dynamically between activated and inactivated states in response to external signals[16]. Activation of NRAS has been associated with the inhibition of drug-induced apoptosis through the PI3K/AKT pathway, ultimately contributing to cellular drug resistance. Our current study has uncovered a compelling connection between the activation of NRAS and the development of paclitaxel resistance in LUAD. The study’s flow diagram is depicted in Figure 1, showing this link. This correlation underscores the promising potential of targeting the NRAS-related pathway as a therapeutic strategy to overcome paclitaxel resistance in lung cancer. Our findings contribute to an enhanced understanding of the intricate molecular landscape of LUAD, offering novel pathways for innovative treatment approaches. 2. Materials and methods 2.1 Data source and processing We systematically assembled a comprehensive dataset for our research from multiple authoritative sources. Our dataset consisted of transcriptome data extracted from the Cancer Genome Atlas (TCGA) database(https://portal.gdc.cancer.gov/), encompassing a total of 555 specimens. This collection comprised 54 normal tissue samples and 501 samples of Lung Adenocarcinoma (LUAD) tissue. To enhance the comprehensiveness of our dataset, we integrated gene profiling data from normal lung tissues, sourced from the Genotypic Tissue Expression (GTEx) database, available at https://www.gtexportal.org/home/index.html. Additionally, we accessed the GSE211374 dataset from the GEO database(https://www.ncbi.nlm.nih.gov/geo/), meticulously curated by the National Center for Biotechnology Information. Furthermore, we incorporated LUAD proteomic data, obtained from the CPTAC data portal (https://cptac-data-portal.georgetown.edu/cptacPublic/). For the genomic profiles of various lung cancer cell lines, we extracted expression data from the CCLE database, accessible at https://sites.broadinstitute.org/ccle. Pharmacogenomic analysis using L1000CDS2 to explore potential therapeutic agents that could be used to treat lung adenocarcinoma the CDSDB database. Subsequent data analysis was executed using the R programming language，version 4.1.1. Data cleaning and ID conversion were performed with the tidyr, dplyr, and rtracklayer R packages. 2.2. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis provides insights into higher-level functions of genes at the molecular level. We performed functional enrichment analysis of NRAS-related genes by using the cluster profile package in R software and visualized the results. 2.3. Correlation analysis of NRAS expression with clinicopathologic features Correlation analysis of NRAS mRNA expression with clinicopathologic features was performed using the ggpubr software package. Box plots were employed to visually represent the associations between NRAS expression and relevant clinicopathological factors, encompassing tumor stage and survival. 2.4.The analysis of tumor immunity estimation resource database The Tumor Immuno Estimation Resource (TIMER) database is a comprehensive online database. We used the TIMER algorithm to analyze the mutation frequencies of NRAS, HRAS and KRAS in each of the TCGA cancer types. To assess the abundance of four distinct immune infiltrates (B cells, CD4+ T cells, neutrophils, and macrophages), we employed the TIMER algorithm. Next, we determined the mRNA expression of NRAS in multiple human cancers. Finally, we used TIMER database to estimate the correlation between the expression of NRAS and abundance of several immune cells. Statistical significance was determined at a threshold of p < 0.05. 2.5. Survival analyses of TCGA cancer data The gene expression profile and the clinical data of patients were downloaded from TCGA. It consisted of 501 tumor samples and 54 normal samples of patients with LUAD. Subsequently, we utilized R software and Strawberry Perl for data processing. Kaplan Meier plotter analysis was used to evaluate the impact of NRAS on the survival rate of LUAD patients. 2.6. Inclusion criteria for LUAD puncture specimens prior to chemotherapy using paclitaxel We developed a classification criterion based on a combination of the RECIST (Response Evaluation Criteria in Solid Tumors) standards, the World Health Organization's guidelines for clinical chemotherapy in lung cancer, and insights from practical clinical experience. This criterion was utilized to analyze patient data from Nantong University Hospital spanning 2021 to 2023, focusing on those who underwent three cycles of paclitaxel chemotherapy. The cohort comprised 13 patients demonstrating paclitaxel resistance and 11 patients exhibiting no resistance to the treatment. Our classification criteria included: 1) Radiologic Progression: Adhering to RECIST guidelines[17], we meticulously reviewed the imaging data of patients to observe changes in tumor size. Patients were categorized as non-resistant if their primary tumor foci either did not increase in size or did not manifest any new metastases post-chemotherapy. Metastases encompassed various forms, including intrapulmonary, brain, and bone metastases. Conversely, patients displaying enlargement in primary foci or emergence of new metastases were classified as resistant. 2) Biomarker Fluctuations: Evaluation of blood tumor markers (CEA, Cyfra21-1, etc) was another pivotal aspect of our classification. Patients whose tumor marker levels either remained stable or decreased following chemotherapy were assigned to the non-resistant group. In contrast, those exhibiting an increase in these biomarker levels were designated as resistant. 2.7. Immunohistochemistry (IHC) LUAD tissue microarray (TMA) consisted of 338 LUAD specimens (Affiliated Hospital of Nantong University) for IHC analysis. Puncture specimen sections prior to chemotherapy with paclitaxel included 24 LUAD specimens for IHC analysis (Affiliated Hospital of Nantong University). IHC staining was performed using NRAS antibody (1:800, Proteintech). TMA sections and sections of pre-chemotherapy puncture specimens were manually scored by visual inspection by two independent researchers who kept clinicopathologic information confidential. Inconsistencies were resolved by discussion. The expression of NRAS was evaluated using the immune response score (IRS), which combines the scores for the percentage of positive cells (0-4: 0%; 1: 1∼25%; 2: 26∼50%; 3: 51∼75%; 4: 76∼100%) and the intensity of staining (0-3: 0: no staining is negative; 1: light yellow is weak; 2: brownish-yellow is intermediate; and 3: tan is strong).The total immunohistochemistry score was the percentage of positive tumour cells score multiplied by the staining intensity score. Low-expression groups were categorized according to a total IHC score of <6, and ≥6 were categorized as high-expression groups. 2.8. GSEA and Single sample GSEA (ssGSEA) The NRAS-associated genomic data in the TCGA database were categorized into positive and negative correlation groups based on risk scores. Gene Set Enrichment Analysis (GSEA) version 4.1.0 software (https://www.gsea-msigdborg/gsea/index.jsp) was utilized for enrichment analysis of hallmark genome and C6 genome. These groups were designated as phenotypes, and we set the number of permutations to 1000 while maintaining default values for all other options. Subsequently, we conducted single-sample Gene Set Enrichment Analysis (ssGSEA) using the GSVA and GSEA BaseR software packages. 2.9. Cell culture The H1299 and PC9 human non-small cell lung cancer cell lines were obtained from the Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, located in Shanghai, China. Cells were cultured in a medium supplemented with 10% fetal bovine serum (FBS) from Gibco (Billings, MT, USA), as well as 100 U/mL of penicillin and 100 µg/mL of streptomycin from Gibco (Carlsbad, CA, USA). Cell maintenance was carried out in a humidified incubator set at 37°C with a 5% CO2 atmosphere. 2.10. Cell transfection and verification We acquired NRAS-specific siRNAs from Gemma Genetics (Shanghai, China). The siRNA sequences synthesized were as follows: NRAS-Homo-491: Sense: 5´-GCCAACAAGGACAGUUGAUTT-3´ Antisense: 5´-AUCAACUGUCCUUGUGGCTT-3´ NRAS-Homo-530: Sense: 5´-GGCCAAGAGUUACGGGGAUUTT-3´ Antisense: 5´-AAUCCCGUAACUCUUUGGCCTT-3´ NRAS-Homo-669: Sense: 5´-GGUUGUAUGGGAUUGCCAUTT-3´ Antisense: 5´-AUGGCAAUCCCAUACAACCTT-3´ Cells were transfected with these siRNA sequences utilizing the Lipofectamine 3000 Transfection Reagent (Invitrogen, USA), Follow the manufacturer's recommendations. To assess the effectiveness of siRNA knockdown, RT-qPCR and Western blot were used to detect the gene knockout efficiency of NRAS. Based on their interference capabilities, NRAS-Homo-530 and NRAS-Homo-669 were selected for further functional cellular assays due to their superior interference efficiency. 2.11. RT-qPCR RNA was extracted from lung tissue with triazole reagent (Invitgen) following the established protocol. Subsequently, complementary DNA (cDNA) synthesis was carried out in accordance with the manufacturer's instructions for the Hifair II 1st Strand cDNA Synthesis SuperMix (11120ES60; Yeasen, Shanghai, China). The synthesized cDNA samples were then subjected to quantitative Real-time Polymerase Chain Reaction (RT-qPCR) analysis utilizing Hieff qPCR SYBR Green Master Mix (Low Rox Plus) (11202ES08; Yeasen, Shanghai, China). This workflow ensured the accurate and reliable assessment of gene expression levels. For each sample, 2μg of RNA was reverse transcribed and the qPCR reaction system was 10μl, of which 2μl of cDNA was used. The fold changes were calculated according to the formula 2−ΔΔCt method. In our work, β-actin was used as the internal control. The primers used were as follows: NRAS: Forward: CTGGGTTCTTCCACAGCACA Reverse: TTCACGTTTGCGGTTTGGTT BIRC2: Forward: AGCACGATCTTGTCAGATTGG Reverse: GGCGGGGAAAGTTGAATATGTA MSH6: Forward: GCAATGCAACGTGCAGATGAA Reverse: ACTTCGCCTAGATCCTTGTGT MSH2: Forward: AGGCATCCAAGGAGAATGATTG Reverse: GGAATCCACATACCCAACTCCAA TOP2A: Forward: ACCATTGCAGCCTGTAAATGA Reverse: GGGCGGAGCAAAATATGTTCC BRCA1: Forward: TTGTTACAAATCACCCCTCAAGG Reverse: CCCTGATACTTTTCTGGATGCC PIK3CA: Forward: AGTAGGCAACCGTGAAGAAAAG Reverse: GAGGTGAATTGAGGTCCCTAAGA APAF1: Forward: GTCACCATACATGGAATGGCA Reverse: CTGATCCAACCGTGTGCAAA BIRC5: Forward: AGGACCACCGCATCTCTACAT Reverse: AAGTCTGGCTCGTTCTCAGTG SLC31A1: Forward: GGGGATGAGCTATATGGACTCC Reverse: TCACCAAACCGGAAAACAGTAG β-actin: Forward: AGTTGCGTTACACCCTTTCTTG Reverse: GCTGTCACCTTCACCGTTCC The above primers were purchased from Bioengineering (Shanghai) Co. 2.12. Western blot Fresh tissue samples were homogenized using homogenizers in a protein lysis buffer containing protease inhibitors to ensure thorough protein extraction. The protein concentrations were quantified using the bicinchoninic acid method. Subsequently, the proteins were electrophoresed on a 12% sodium dodecyl sulfate polyacrylamide gel (SDS-PAGE) (cat. #XP00100BOX; Thermo, USA). Following electrophoresis, the proteins were transferred onto polyvinylidene difluoride (PVDF) membranes (cat. #88518; Thermo). To prevent non-specific binding, the membranes were blocked with 5% skim milk in Tris-Buffered Saline containing Tween-20 (TBS-T) and then incubated with primary antibodies overnight at 4°C. Primary antibodies included rabbit anti-NRAS (dilution 1:2000; cat. 10724-1-AP; ProteinTech) and mouse anti-β-actin (dilution 1:25000; cat. 66009-1-lg; ProteinTech). After primary antibody incubation, incubate the membrane with secondary antibody for 1-hour, secondary antibody including Goat Anti-Rabbit IgG (dilution 1:8000; cat. SA00001-2; ProteinTech) and Goat Anti-Mouse IgG (dilution 1:8000; cat. SA00001-1; ProteinTech). Protein bands were visualized using the enhanced chemiluminescence technique. Band densities were quantified with Image J software (National Institutes of Health, Bethesda, MD) and subsequently normalized to β-actin. Relative protein levels were calculated as the density ratios of the target protein to β-actin. 2.13. Cell viability assay H1299 cells and PC9 cells, including unmodified cells and those transfected with NRAS-Homo-530 and NRAS-Homo-669, were seeded in 96-well culture plates at a density of 1500 cells per well. After a 24-hour incubation period, the medium was refreshed with new medium supplemented with varying concentrations of paclitaxel (30 mg/5 ml, Nanjing Luye Pharmaceutical Co., Ltd): 0 µg/ml, 0.003 µg/ml, 0.03 µg/ml, 0.3 µg/ml, 30 µg/ml, and 300 µg/ml. This medium, containing the respective paclitaxel concentrations, was renewed every 48 hours. On the seventh day post-inoculation, cell viability was assessed. CCK-8 reagent (C0037, Limited Company., Shanghai, China) was added to each well and the plates were further incubated for 2 hours in a 5% CO2 humidified atmosphere. Subsequently, the absorbance at 450 nm was measured using a spectrophotometric enzyme assay reader. Each condition had three replicate wells, and the mean absorbance value was computed for analysis. The half maximal inhibitory concentration (IC50) for both the NRAS knockdown and control cells was deduced by comparing cell viability percentages against those of untreated controls. 2.14. Wound healing assay The cells in the H1299 NC group, NRAS-Homo-530 interference group, and NRAS-Homo-669 interference group were each placed into separate 6-well culture plates, with a cell density of 5×10^5 cells per well, and PC9 cells were similarly treated. Subsequently, they were cultured for a duration of 24 hours at 37°C in an environment with 5% CO2. Following incubation, the medium was aspirated, and a uniform scratch was created in the cell monolayer using a 10-µl pipette tip. Cells were then gently rinsed twice with PBS to remove any detached cells. Subsequently,2 ml of RPMI 1640 medium was added to each well. Scratch wound images were captured at time points of 0, 24 and 48 hours post-scratching. Each experimental condition had three replicate wells. The migration distance of the cells into the scratched region was quantified over the designated time periods. The resultant data were normalized and presented as a migration index, calculated as the ratio of the migration distance of the experimental groups to that of the NC group. 2.15. Statistical analysis Using R programming language (version 4.1.1) for statistical analysis, GraphPad Prism Software (version 9.0, Graph Pad Software Inc., La Jolla, CA, USA) and SPSS (version 26.0, SPSS, Inc., Chicago, IL, USA). Pearson's correlation coefficients were computed with case weighting to derive P-values. T-tests were utilized to compare two groups, whereas one-way analysis of variance (ANOVA) was employed for comparisons involving multiple groups, with the use of chi-square testing when appropriate. For post-hoc analyses of significant ANOVA results, Pairwise comparison using Q test in instances of non-significance, the non-parametric rank sum test was adopted. Survival data were depicted using the Kaplan-Meier method, and statistical differences between the curves were assessed using the log-rank test. A significance threshold of P < 0.05 was applied to all statistical analyses. 3. Results 3.1. Paclitaxel resistance is associated with the RAS signaling pathway, and NRAS is a high-quality target for the treatment of lung adenocarcinoma. In our investigation of paclitaxel resistance in lung adenocarcinoma (LUAD), we utilized the Genome Expression Omnibus (GEO) database to identify relevant datasets. Our search yielded dataset GSE211374 as a potential source of insight (available at https://www.ncbi.nlm.nih.gov/geo/). To ensure the reliability of this dataset, we recalculated the raw data using both the server’s resources and the Linear Models for Microarray Data (LIMMA) pipeline, depicting our findings in Supplementary Figure 1A-E. We performed a differential expression analysis comparing paclitaxel-resistant and non-resistant cohorts within GSE211374 to identify genes exhibiting significant expression differences. Subsequently, the identified genes underwent pathway enrichment analysis via the Kyoto Encyclopedia of Genes and Genomes (KEGG) to elucidate their associated pathways. Our analysis revealed a significant enrichment of these genes across several pathways, including the RAS signaling pathway, T cell receptor signaling pathway, and PI3K-Akt signaling pathway. Figure 2A presents this pronounced enrichment, particularly highlighting that of the RAS signaling pathway. This enrichment prompted our hypothesis that activating the RAS signaling pathway potentially contributes to the development of paclitaxel resistance in LUAD. Within the RAS gene family encompassing KRAS, NRAS, and HRAS, we explored their correlation with the clinical characteristics of LUAD using the Cancer Genome Atlas (TCGA) database (accessible at https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Our exploration revealed that NRAS exhibited higher expression levels than KRAS and HRAS and showed an increase in expression aligning with the advancement of clinical stages (Figures 2B and C). Moreover, elevated NRAS expression correlated with a poorer prognosis (Figure 2D). In the analysis of mutation frequencies across NRAS, KRAS, and HRAS in pan-cancer within the TIMER2.0 database, we found that the mutation frequencies of NRAS were lower than that of KRAS and HRAS in LUAD (Figure 2E, Supplementary figures 2A and B). Importantly, the presence or absence of a mutation in NRAS did not significantly correlate with the survival prognosis of patients (Supplementary figure 2C). We also generated proteomic correlation heat maps of NRAS protein expression, associating NRAS protein expression with clinicopathological features utilizing the CPTAC database (Figure 2F). In summary, our data strongly suggested that NRAS as a high-quality research target in LUAD, especially in the context of paclitaxel resistance. 3.2. NRAS is highly up-regulated in LUAD and correlates with poor clinical outcomes. To investigate the prognostic implications and potential therapeutic significance of NRAS in lung adenocarcinoma (LUAD), we conducted an extensive analysis of NRAS expression using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project datasets. Our analyses revealed significant overexpression of NRAS in LUAD tissue compared to adjacent non-tumorous tissue, a finding that persisted in both paired and unpaired analyses (Figure 3A-C). Further evaluation of NRAS as a prognostic and diagnostic marker was conducted via Receiver Operating Characteristic (ROC) analysis, indicating a robust predictive accuracy for LUAD, as evidenced by an area under the ROC curve (AUC) of 0.793 (Figure 3D). In addition, by Kaplan-Meyer survival curve analysis, we divided the patients into NRAS high expression group (the largest 25%) and low expression group (the smallest 75%) according to the upper quartile, and into NRAS high expression group (the largest 75%) and low expression group (the smallest 25%) according to the lower quartile. The results all showed that high NRAS expression was associated with a significant reduction in overall survival (OS) in LUAD patients (Figure 3E and F). Moreover, our analyses of overall survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI) consistently indicated that elevated NRAS expression was associated with adverse outcomes (Supplementary Tables 1-3). To confirm our bioinformatic findings, we conducted immunohistochemical (IHC) staining on cancer specimens. The IHC results aligned with our computational findings, demonstrating predominant cytoplasmic localization of NRAS in LUAD cells and significantly higher expression levels in tumor than adjacent non-neoplastic tissue (Figure 3G). Using lung adenocarcinoma tissue microarray (TMA), we also observed correlations between NRAS gene expression and vascular thrombus, tumor size, degree of differentiation, T-stage, N-stage and overall survival. However, we noted comparatively weaker associations with other clinicopathological parameters (Table 1). These comprehensive findings affirm the prognostic significance of NRAS expression in LUAD and suggest its potential utility in guiding therapeutic decisions. This highlights the necessity for further research into NRAS as a target for intervention in LUAD. 3.3. Enrichment analysis of KEGG pathway related to NRAS. To unravel the underlying mechanism of paclitaxel resistance mediated by NRAS in lung adenocarcinoma (LUAD) cells, we completed an analysis targeting NRAS-related genes. Initially, heat maps were generated to visualize the correlation between NRAS-related genes and survival status and clinical stage using the TCGA database (Figure 4A). We then subjected these NRAS-related genes to Gene Set Enrichment Analysis (GSEA), revealing their significant abundance in key pathways, notably the G2/M checkpoint and vascular endothelial growth factor (VEGF) signaling pathway (Figure 4B and C). To delve deeper into the mechanistic aspects, we analyzed differential expression to identify genes that exhibited variation between high and low NRAS expression cohorts (Figure 4D). Interacting these differentially expressed genes with those strongly associated with NRAS led to the identification of a subset comprising 97 genes (Figure 4E). Subsequently, we constructed a Protein-Protein Interaction (PPI) network for these 97 genes, revealing a dense web of interaction, suggesting a complex regulatory network (Figure 4F). Further KEGG enrichment analysis of this gene network highlighted their significant involvement in essential cellular pathways, including those regulating the cell cycle and DNA damage repair mechanisms (Figure 4G and H). In conclusion, our comprehensive analysis of NRAS-related genes in LUAD has shed light on the importance of the G2/M checkpoint and VEGF signaling pathways. The identified gene network and its association with critical cellular processes provide a foundation for future targeted therapies aimed at overcoming resistance in lung adenocarcinoma treatment. 3.4. NRAS related immune infiltration We investigated the correlation between several immune cells and NRAS utilizing the TIMER2.0 database. Our analysis revealed a significant negative correlation with B cells and CD4+ T cells while demonstrating a significant positive correlation with neutrophils and macrophages (Figure 5A-D). We then conducted ssGSEA enrichment analysis of NRAS with 24 types of immune cells within the TCGA database. This analysis showed that the high and low expression groups of NRAS exhibited a significant correlation with several immune cells, including B cells, CD8 T cells, eosinophils, macrophages, mast cells, neutrophils, CD56 bright NK cells, NK cells, Plasmacytoid DC, T Helper cells, T cells, TFH, Tgd, Th17 cells, and Th2 cells (Figure 5E). Further, ssGSEA enrichment analysis revealed a significant correlation between NRAS and various immune cell types, particularly Th2 cells, p DC, Th17 cells, CD56 bright NK cells, CD8 T cells, and T follicular helper cells (Figure 5F). This evidence indicates that targeting NRAS could hold promise for immunotherapeutic interventions in managing LUAD. 3.5. Knockdown of NRAS increases the sensitivity of LUAD to paclitaxel Firstly, we analyzed NRAS expression across four lung adenocarcinoma cell lines within the TCGA database, highlighting higher NRAS expression in the H1299 and PC9 cell lines (Figure 6A). To corroborate our findings, a series of laboratory experiments was conducted. We employed three distinct small interfering RNAs (siRNAs) to achieve NRAS knockdown in the H1299 and PC9 lung adenocarcinoma cell lines. The efficacy of NRAS silencing was confirmed through quantitative polymerase chain reaction (qPCR) and Western blot (WB) analyses (Figures 6B-E). Among the tested siRNAs, SI-530 and SI-669 exhibited potent knockdown efficiency, prompting their selection for subsequent experiments. We then introduced paclitaxel to the control and NRAS-silenced groups in H1299 and PC9 cell lines. Post-treatment, we utilized the CCK8 assay to assess cellular viability. Strikingly, the NRAS-silenced group exhibited a significant reduction in the half-maximal inhibitory concentration (IC50) of paclitaxel (P<0.01), suggesting that inhibiting NRAS potentiates the cytostatic effects of paclitaxel in both cell lines (Figure 6F and G). Additionally, wound healing assays were conducted to evaluate cell migration. Notably, NRAS knockdown significantly inhibited cell migration at 24 and 48 hours post-treatment in both cell lines (Figure 6H and I). These findings underscore the role of NRAS in modulating the responsiveness of LUAD cells to paclitaxel’s anti-migratory effects in both cell lines. Further investigation involved analyzing lung needle biopsies from 24 patients with lung adenocarcinoma prior to paclitaxel chemotherapy. Immunohistochemical staining revealed markedly lower NRAS expression in tumor tissues from the non-resistant group compared to the drug-resistant group (Figure 6J). Other clinical characteristics of the patients are shown in Table 2. These comprehensive analyses provide compelling evidence that NRAS significantly influences paclitaxel resistance in LUAD. Furthermore, we performed pharmacogenomic analyses using L1000CDS2 in the CDSDB database to predict potential therapeutic agents for the treatment of lung adenocarcinoma (Figure 6K). The drug prediction outcomes present several potential therapeutic options for LUAD, which warrant further exploration. 3.6. Strong correlation between NRAS and genes related to paclitaxel resistance Our investigative approach took a multifaceted trajectory to elucidate further the relationship between NRAS and paclitaxel resistance in lung adenocarcinoma (LUAD). Initially, we queried the TCGA database to identify NRAS-related genes. Subsequently, we accessed the PharmaGkb database (https://www.pharmgkb.org/) to retrieve relevant gene sets associated with therapy drugs for LUAD. Conducting Gene Set Enrichment Analysis (GSEA) on these datasets indicated significant enrichment of NRAS-related genes within the Pa450761 gene set, which is linked to paclitaxel resistance. This substantiated the correlation between NRAS and paclitaxel resistance in LUAD (Figures 7A and B). Our research further identified nine genes strongly associated with NRAS within the context of paclitaxel resistance in LUAD (Figure 7C). To validate these findings, we performed RT-qPCR analyses to assess the expression levels of these genes post-NRAS knockdown. Remarkably, BIRC2, MSH6, MSH2, TOP2A, and BRCA1 expression exhibited concurrent downregulation with NRAS (Figure 7D-H), affirming their correlation. However, PIK3CA, APAF1, BIRC5, and SLC31A1 did not correlate significantly with NRAS expression (Supplementary Figure 3A-D). This provided further insight into the NRAS-related molecular network associated with paclitaxel resistance. These findings underscore the potential of NRAS as a therapeutic target to enhance paclitaxel's efficacy in treating lung adenocarcinoma. 4. Discussion Lung adenocarcinoma stands out as one of the most prevalent cancer types, affecting a substantial number of patients at intermediate to advanced disease stages[18, 19]. Paclitaxel, a cornerstone chemotherapeutic drug, continues to play a pivotal role in the treatment of lung adenocarcinoma. However, a concerning trend of increasing resistance to paclitaxel has emerged in recent observations, with many patients exhibiting varying levels of drug tolerance[20]. This study presents a comprehensive analysis of NRAS expression and its impact on paclitaxel resistance in lung adenocarcinoma (LUAD). Our findings corroborate previous assertions of NRAS as a significant oncogenic driver and contribute to advancing the current understanding of its role in chemoresistance, offering insights that could steer future therapeutic strategies. In our study, we conducted pathway enrichment analysis utilizing bioinformatics approaches on patients undergoing paclitaxel chemotherapy. This analysis revealed a critical role of the RAS signaling pathway in the development of paclitaxel resistance. The RAS signaling pathway is vital in various cellular functions, such as growth, division, survival, and migration[21]. Within the RAS protein family, comprising KRAS, NRAS, and HRAS, the mutation frequency of KRAS in lung adenocarcinoma can be as high as 32%. Developing therapies targeting KRAS poses a significant obstacle due to the absence of drug-binding sites, rendering it a complex and challenging therapeutic target. Interestingly, KRAS mutations are more common in lung adenocarcinoma than in other adenocarcinomas, whereas NRAS mutations are less common in lung adenocarcinoma. While a large body of literature exists on NRAS-mutant melanoma and its association with poor disease prognosis, especially in advanced stages, therapeutic targets often involve combined inhibition of MAPK signaling and CDK4/6-driven cell cycle progression[22]. However, the role of NRAS in lung adenocarcinoma remains relatively under-investigated. Our findings, derived from a fusion of TCGA and GTEx datasets, reveal a significant upregulation of NRAS in tumor tissues compared to adjacent non-tumorous counterparts. This observation is consistent with previous research emphasizing NRAS’s oncogenic role across multiple cancer types[23]. The consistent increase in expression observed across multiple independent datasets highlights NRAS’s potential as a biomarker for LUAD, further confirming its diagnostic value, as indicated by the area under the ROC curve of 0.793. This diagnostic potential is particularly promising, considering the challenges associated with early detection of LUAD. Moreover, immunohistochemical analyses further substantiate these findings, revealing a predominant cytoplasmic NRAS localization pattern consistent with the protein’s involvement in signal transduction pathways. Our immunohistochemical analysis suggests that aberrant NRAS expression may affect multiple downstream pathways. This finding underscores the significance of NRAS’s subcellular distribution in its interactions with various signaling molecules, consequently impacting cell proliferation and survival. The Kaplan-Meier survival curves illustrate the prognostic implications of NRAS expression. Our study highlights that patients exhibiting elevated NRAS levels experienced significantly poorer overall survival rates. Such data indicate the aggressive nature of NRAS-driven tumors, emphasizing the urgent need for targeted interventions in this context. Upon dissecting pathways related to NRAS, our enrichment analysis highlighted the involvement of the G2/M checkpoint and VEGF signaling pathways. The G2/M checkpoint is crucial for maintaining genomic integrity, and its disruption is a recognized hallmark of cancer[24]. Meanwhile, the VEGF pathway’s role in angiogenesis and its implications in tumor progression and metastasis have been well-documented[25, 26]. The enrichment observed in the G2/M checkpoint and VEGF signaling pathways is noteworthy, suggesting that NRAS may contribute to LUAD pathogenesis through multiple pathways, potentially involving the promotion of angiogenesis and facilitation of cell cycle dysregulation. In unraveling these mechanisms, our gene correlation analysis and subsequent KEGG pathway enrichment revealed a strong association of NRAS-related genes with pathways critical for cell cycle progression and DNA repair. Enriching NRAS-related genes within these pathways establishes a mechanistic link between NRAS overexpression and the recognized hallmarks of cancer. Notably, our pathway enrichment analysis indicated potential mediation of paclitaxel resistance through these pathways. The aberrant regulation of these pathways can lead to unchecked cellular proliferation and a failure to rectify DNA damage, cultivating an environment conducive to the development and progression of cancer. The dysregulation of these pathways has been implicated in resistance to various chemotherapeutic agents, including paclitaxel[27, 28]. Our experiments involving NRAS knockdown displayed a concurrent decrease in genes associated with paclitaxel resistance, consequently enhancing paclitaxel’s inhibitory impact on lung adenocarcinoma cell proliferation and migration. These findings underscore the potential of NRAS targeting to augment paclitaxel sensitivity in lung adenocarcinoma cells. The protein-protein interaction network further elucidated the complexity of interactions governed by NRAS, indicating its potential role as a central point in the oncogenic signaling network. The convergence of differentially expressed genes with NRAS-associated genes yielded a set of 97 genes, forming a densely connected network. This network’s complexity typifies a robust biological system capable of sustaining oncogenic signaling pathways and potentially resisting therapeutic interventions. Such robustness may offer a plausible explanation for the development of paclitaxel resistance in LUAD. Our study’s strength lies in the integration of multi-omic data and the validation of findings through both in silico and immunohistochemical analyses. However, we do acknowledge certain limitations inherent to our study design. The study's retrospective nature and reliance on public databases may introduce certain inherent biases. Thus, prospective studies are crucial to validate our findings and delve deeper into the therapeutic targeting potential of NRAS and its associated gene network. It’s also important to note that our investigations were primarily conducted at the cellular level. Future experiments should incorporate animal models to simulate in vivo conditions more accurately. Additionally, our exploration focused on a subset of NRAS functionalities, leaving several potential roles in lung adenocarcinoma unexplored. Future studies should encompass a broader investigation of NRAS functions in lung adenocarcinoma. 5. Conclusion our research delineates a clear association between NRAS overexpression and paclitaxel resistance in LUAD, providing pivotal preliminary insights into NRAS’s involvement in lung adenocarcinoma. It elucidates the association between NRAS and paclitaxel resistance, hinting at potential drug interventions that could enhance paclitaxel’s efficacy. This study offers novel therapeutic possibilities for patients resistant to paclitaxel and paves the way for subsequent investigative endeavors. Abbreviations LUAD, Lung adenocarcinoma; NSCLC, non-small cell lung cancer; MTOC, microtubule organization center; TCGA, The Cancer Genome Atlas; GTEx, Genotypic Tissue Expression; KEGG, Kyoto Encyclopedia of Genes and Genomes; TIMER, Tumor Immuno Estimation Resource; RECIST ,Response Evaluation Criteria in Solid Tumors; IHC, immunohistochemistry; IRS, immune response score; GSEA; Gene Set Enrichment Analysis; ssGSEA, Single sample GSEA; FBS, fetal bovine serum; cDNA, complementary DNA;RT-qPCR, Reverse Transcription-Polymerase Chain Reaction ;WB, Western Blot; PVDF, polyvinylidene difluoride; ROC, Receiver Operating Characteristic; OS, overall survival; DSS, Disease-Specific Survival; PFI, Progression-Free Interval; TMA, tissue microarray; VEGF, vascular endothelial growth factor. Declarations Supplementary Information The authors declare that the data supporting the findings of this study are available within the article/Supplementary material. Ethics approval and consent to participate This protocol's design adheres to the Declaration of Helsinki principles and received approval from the Ethics Committee of Affiliated Hospital of Nantong University (reference number 2022-L078). Availability of data and materials The datasets analyzed during the current study are available at the database URLs mentioned in the material and methods. Acknowledgements Not applicable. Author contributions Conceptualization: Taoming Mo, Shuang Zhang, Qishuang Wei, Yifei Liu. Methodology and Investigation: Taoming Mo, Shuang Zhang, Qishuang Wei, Yali Zhang. Visualization: Taoming Mo, Shuang Zhang, Li Tong, Sichu Wang, Lijuan Tang. Funding acquisition: Taoming Mo, Tingting Bian, Jianguo Zhang, Jun Zhu, Yifei Liu. Project administration and Supervision: Tingting Bian, Yifei Liu, Shaolei Lu. Writing – original draft: Shuang Zhang, Taoming Mo. Writing – review & editing: Taoming Mo, Shuang Zhang, Ryan Liu, Shaolei Lu, Yifei Liu. All authors read and approved the final manuscript. Funding This study was funded by grants from National Natural Science Foundation of China (No. 82273422), Postgraduate Research Project and Practice Innovation Program of Jiangsu province (No. SJCX22_1639),2022 Nantong Basic Research Plan Project (JC12022016, JC22022020, MS22022017) and 2023 Nantong Social Livelihood Science and Technology Plan Project (MS2023067). Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. 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Tables Table 1 Relationship between NRAS expression and pathological parameters of LUAD TMA. Clinical pathology parameters Total NRAS low expression group （ n=167 ） NRAS high expression group （ n=171 ） X² p-value age （ years ） >65 ≤65 154 184 68 99 86 85 2.7481 0.09737 Gender Male Female 146 192 67 100 79 92 1.0368 0.3086 Vascular Tumor Embolus 1 0 88 250 24 143 64 107 22.14 2.54E-06 Airway Dissemination 1 0 112 226 52 115 60 111 0.67532 0.4112 Pleural Involvement 1 0 125 213 56 111 69 102 1.4053 0.2358 Tumor Size(cm) <3 ≥3 208 130 121 46 87 84 15.721 7.34E-05 Degree of Differentiation Well Moderate Poor 14 230 94 10 138 19 4 92 75 45.092 1.62E-10 T T1 T2 T3 T4 248 74 11 5 139 22 5 1 109 52 6 4 17.637 5.23E-04 N N0 N1 N2 228 47 63 121 7 39 107 40 24 27.558 1.04E-06 M M0 M1 333 5 167 0 166 5 3.1531 0.07578 Stage Ⅰ Ⅱ Ⅲ+Ⅳ 215 52 71 137 14 16 78 38 55 48.65 2.73E-11 Overall Survival 1 0 272 66 148 19 124 47 12.945 3.21E-04 Table 2 Association of NRAS expression with blood tumor markers and radiological progression in LUAD pre-chemotherapy puncture specimens. Case Age （ year ） Gender Radiological Progression Changes in Blood Tumor Markers IHC Scoring Pre-treatment Tumor Size （ cm 2 ） Post-treatment Tumor Size （ cm 2 ） Metastatic Status Pre-treatment CEA （ ng/ml ） Post-treatment CEA （ ng/ml ） Pre-treatment Cyfra21-1 （ ng/ml ） Post-treatment Cyfra21-1 （ ng/ml ） Paclitaxel - Sensitive Group 1 55 Male 3.5*2.8 2.9*2.4 None 7.2 3.8 8.55 2.04 3 2 56 Male 7*6.2 4*3.5 None 137.3 71.5 38.7 2.66 4 3 61 Male 3.6*2.6 3.2*2.6 None 23.0 9.6 49.98 6.84 3 4 65 Female 3.7*2.1 2.8*1.7 None 8.3 3.4 9.48 1.25 3 5 75 Male 2.1*1.8 1.4*1.0 None 377.4 218.4 11.33 2.2 8 6 57 Male 7.0*6.4 6.2*5.2 None 6.6 2.4 8.87 2.11 6 7 78 Male 2.8*2.6 2.0*1.9 None 5.5 2.9 5.79 1.3 3 8 74 Female 3.6*2.8 2.9*2.5 None 235.2 4.1 8.12 1.65 4 9 73 Female 3.4*3.0 2.6*2.4 None 25.0 3.5 9.06 3.48 6 10 65 Male 1.3*1.0 1.2*0.9 None 7.0 1.2 3.86 1.16 4 11 54 Female 3.5*2.4 3.5*2.3 None 496.8 7.6 7.90 2.16 4 Paclitaxel -Resistant Group 1 57 Male 2.6*1.7 4.3*2.7 Lymph Node Metastasis 11.6 24.5 11.66 13.17 6 2 71 Male 5.3*3.2 5.5*3.4 Pleural Metastasis 23.6 64.2 5.90 13.1 6 3 67 Female 3.9*5.1 3.9*5.1 Hepatic Metastasis 11.9 156.1 9.04 >100 12 4 72 Male 4.2*2.8 4.5*3.1 Adrenal Metastasis 6.2 16.1 3.68 6.52 12 5 61 Male 3.8*2.9 3.9*3.0 Lymph Node Metastasis 14.1 84..5 21.34 31.15 12 6 69 Female 4.6*3.3 7.8*5.8 Lumbar Spine Metastasis 21.8 33.6 9.55 18.61 6 7 65 Female 2.8*1.7 4.4*2.4 Lumbar Spine Metastasis 14.7 46.0 10.91 31.59 12 8 58 Female 5.1*4.8 6.9*6.0 Brain Metastasis 31.7 68.6 3.86 11.02 12 9 59 Male 2.5*2.1 4.5*4.5 Brain Metastasis 166.1 >1500 8.05 >100 6 10 68 Female 1.5*0.8 1.8*1.2 Intrapulmonary Metastasis 102.3 >1500 6.72 >100 12 11 57 Male 6.6*4.1 6.6*5.2 Lymph Node Metastasis 18.3 27.2 19.23 25.53 12 12 64 Male 2.9*3.3 6.1*3.5 Lumbar Spine Metastasis 27.8 145.1 31.99 43.92 12 13 48 Male 3.4*2.7 3.6*3.1 Lumbar Spine Metastasis 31.0 81.2 21.07 51.34 8 Additional Declarations No competing interests reported. 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Island Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaolei","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-04-22 14:06:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4306414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4306414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55630183,"identity":"d7bc6779-9810-4c08-8711-e99cf75f728c","added_by":"auto","created_at":"2024-04-30 19:17:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168787,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall research ideas of this paper were shown as follows.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/24cdbbbeea508a591f55e7ba.png"},{"id":55630199,"identity":"2a03ad9b-8b9f-44be-973c-28cfa2590373","added_by":"auto","created_at":"2024-04-30 19:17:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":735963,"visible":true,"origin":"","legend":"\u003cp\u003ePaclitaxel resistance is associated with the RAS signaling pathway and NRAS is a high-quality target for lung adenocarcinoma therapy.\u003c/p\u003e\n\u003cp\u003e(A)KEGG pathway enrichment analysis in GEO. (B-D) Relationship between NRAS, KRAS, HRAS and clinicopathological parameters in TCGA. (E)NRAS mutation rate analysis in TIMER2.0. (F)Relationship between NRAS and clinicopathological parameters in CPTAC. (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/8689c9a5e6ccde4008c1049c.png"},{"id":55630184,"identity":"03743750-c542-4257-b84b-aba4d35ea5a6","added_by":"auto","created_at":"2024-04-30 19:17:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1225455,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis and survival analysis of NRAS in LUAD.\u003c/p\u003e\n\u003cp\u003e(A)Relative mRNA expression of NRAS in the TCGA-LUAD database. (B) Relative mRNA expression of NRAS in LUAD tissues and their paired normal tissues from the TCGA database. (C) Relative mRNA expression of NRAS in TCGA-LUAD+GTEx database. (D) ROC curve of LUAD prognostic model. (E,F)Survival analysis related to NRAS high and low expression groups in TCGA. (G) NRAS expression in LUAD tissue (a,b) and normal lung tissue (c,d)in LUAD TMA. (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/90455fedfc249946c05dea91.png"},{"id":55630200,"identity":"d380a39f-aad0-4fd0-b0df-1ba3b94b48d3","added_by":"auto","created_at":"2024-04-30 19:17:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2147358,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of NRAS-related pathways.\u003c/p\u003e\n\u003cp\u003e(A) Heat map for NRAS correlation analysis. (B,C)GSEA enrichment analysis of NRAS-related genes. (D)Volcano map of NRAS-related differential genes. (E)Venn diagram of the DEGs and NRAS-related genes, revealing 97 intersected genes. (F)Protein-Protein interaction network map of 97 genes. (G,H)KEGG pathway enrichment analysis in TCGA.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/e805b161bf79ff98f7603721.png"},{"id":55630196,"identity":"75decc1c-aa8e-4914-a9f9-385fa52a47f4","added_by":"auto","created_at":"2024-04-30 19:17:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":793924,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of NRAS with immune cell infiltration in LUAD.\u003c/p\u003e\n\u003cp\u003e(A-D) Correlation of NRAS expression level with B cells (A), CD4+ T cells (B), neutrophils (C) , macrophages (D) infiltration levels in LUAD. **p \u0026lt;0.01, ***p \u0026lt; 0.001. (E) The ssGSEA analysis of the differences between the two groups with high and low NRAS expression and 22 types of immune cells. (F) The ssGSEA analysis of the correlation between NRAS expression and 22 types of immune cells.(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/e6fce6077799ae3a9cd0cb30.png"},{"id":55630197,"identity":"1bfce846-d937-4996-a297-358d6a589eea","added_by":"auto","created_at":"2024-04-30 19:17:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1385611,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and functional analysis of NRAS gene in different cell lines and assessment of paclitaxel sensitivity and resistance.\u003c/p\u003e\n\u003cp\u003e(A)Gene expression levels of NRAS in multiple cell lines were analyzed in the CCLE database. (B,D)q-PCR(B) and Western Blot(D) validation of NRAS knockdown in H1299 cell line with 3 siRNAs. (C,E)q-PCR(C) and Western Blot(E) validation of NRAS knockdown in PC9 cell line with 3 siRNAs. (F)Cell viability was assessed using the CCK8 assay in H1299 cell line, IC50=3.038ug/ml in NC, IC50=0.509ug/ml in Si-NRAS-530 and IC50=1.525ug/ml in Si-NRAS-669. (G)Cell viability was assessed using the CCK8 assay in PC9 cell line, IC50=3.415ug/ml in NC, IC50=0.584ug/ml in Si-NRAS-530 and IC50=1.739ug/ml in Si-NRAS-669. (H)Representative data from three sets of wound healing migration assays in the H1299 cell line (Paclitaxel at a concentration of 3ug/ml was used for treatment in each group). (I)Representative data from three sets of wound healing migration assays in PC9 cell line(Paclitaxel at a concentration of 3.5ug/ml was used for treatment in each group). (J)NRAS expression in Paclitaxel-sensitive group and Paclitaxel-resistant group. (K)Petalograms of drug predictions. (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/f35281fc332dc507dfaa52c3.png"},{"id":55630201,"identity":"8086a3e9-461b-447d-a412-93d64606b419","added_by":"auto","created_at":"2024-04-30 19:17:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":447075,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between NRAS and genes related to paclitaxel resistance.\u003c/p\u003e\n\u003cp\u003e(A,B) GSEA analysis of NRAS-related genes and drug-related genes.(C) Correlation analysis of NRAS and paclitaxel resistance-related genes.(D-H)q-PCR validation of knockdown of NRAS with five genes significantly associated with paclitaxel resistance, including BIRC2(D), MSH6(E), MSH2(F), TOP2A(G) and BRCA1(H).(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/fd30bdb4535ccf3f4a989fe7.png"},{"id":62740626,"identity":"9e89c48c-97d9-49d1-a490-03c308486bf8","added_by":"auto","created_at":"2024-08-19 02:45:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9010938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/b1e46e6b-9a3e-4e2f-92a2-99c9ba1c3c42.pdf"},{"id":55630189,"identity":"5be736c1-cddc-460c-a93f-ce19e5f4c7f5","added_by":"auto","created_at":"2024-04-30 19:17:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1306323,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4306414/v1/7110dcac10132bb4bf43be24.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeting NRAS Inhibits Cancer Cell Growth and Enhances Paclitaxel Sensitivity in Lung Adenocarcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer stands as the leading cause of cancer-related mortality, responsible for approximately 1.8 million deaths, representing 18% of all cancer-related fatalities[1, 2]. Non-small cell lung cancer (NSCLC) accounts for about 85% of newly diagnosed lung cancer cases, posing a significant challenge with a 5-year survival rate of less than 20%[3, 4]. Among NSCLC subtypes, lung adenocarcinoma (LUAD) has emerged as the most prevalent histological subtype, with an increasing incidence observed among young women and individuals who have never smoked. Alarmingly, nearly 70% of patients receive their diagnosis at an advanced disease stage. Current treatment modalities encompass a range of approaches, including surgical interventions, chemotherapy, and radiotherapy. Despite advancements in surgical techniques, the 5-year survival rate for surgically resectable NSCLC patients has seen only marginal improvements in recent decades[5]. In the face of progress in targeted therapies and immunotherapy, chemotherapy remains the cornerstone of treatment for advanced lung adenocarcinoma[6]. Paclitaxel holds an important role as a first-line chemotherapy drug. However, clinical observations indicated an increasing resistance to paclitaxel in recent years, necessitating an urgent search for targets that could increase the sensitivity of LUAD cells to this drug.\u003c/p\u003e\n\u003cp\u003ePaclitaxel plays a pivotal role as a first-line chemotherapy drug in treating non-small-cell lung cancer (NSCLC) and various other malignancies. Its mechanism of action involves acting as a microtubule stabilizer, exerting anti-cancer effects by preventing microtubule depolymerization. This reduces microtubule dynamics within the mitotic spindle, leading to G2/M cell cycle arrest and initiating apoptosis, ultimately impeding tumor cell mitosis and growth[7, 8]. A notable aspect of paclitaxel is its induction of \u0026alpha;-tubulin acetylation, resulting in the acetylated \u0026alpha;-tubulin\u0026rsquo;s localization within the microtubule organization center (MTOC). This action potentially alters microtubule dynamics[9]. However, in the context of advanced LUAD, the efficacy of paclitaxel is often compromised by the emergence of drug resistance mechanisms. These mechanisms include the overexpression of cellular pumps, such as P-glycoprotein[10], microtubule dynamics alterations[11], and apoptotic pathways modifications[12]. Such changes can diminish cell responsiveness to drug-induced death signals, necessitating the exploration of novel therapeutic avenues. While some studies have investigated the potential synergistic effects of combining paclitaxel with other drugs to enhance its efficacy, the 5-year survival rate for LUAD patients remains disappointingly below 20%. Consequently, a pressing need exists to identify targets that could increase the sensitivity of LUAD cells to paclitaxel.\u003c/p\u003e\n\u003cp\u003eRAS genes stand out as the most frequently mutated oncogenes in cancer. These genes wield significant power within the RAS pathway, assuming a central role in various biological processes[13]. These include cell growth, proliferation, differentiation, migration, and survival. The dysregulated activity of this pathway is a hallmark of cancer, often leading to uncontrolled cell proliferation. The relationship between paclitaxel resistance and the RAS pathway has been reported in previous studies including bladder and colorectal[14, 15]. However, the association has been relatively unexplored in lung adenocarcinoma. RAS oncogenes encode a family of membrane-associated proteins critical in regulating signal transduction upon binding to various membrane receptors. Among these, NRAS, KRAS, and HRAS form the core of the RAS gene family. NRAS, an isomeric family member, functions as a small G protein transitioning dynamically between activated and inactivated states in response to external signals[16]. Activation of NRAS has been associated with the inhibition of drug-induced apoptosis through the PI3K/AKT pathway, ultimately contributing to cellular drug resistance.\u003c/p\u003e\n\u003cp\u003eOur current study has uncovered a compelling connection between the activation of NRAS and the development of paclitaxel resistance in LUAD. The study\u0026rsquo;s flow diagram is depicted in Figure 1, showing this link. This correlation underscores the promising potential of targeting the NRAS-related pathway as a therapeutic strategy to overcome paclitaxel resistance in lung cancer. Our findings contribute to an enhanced understanding of the intricate molecular landscape of LUAD, offering novel pathways for innovative treatment approaches.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data source and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe systematically assembled a comprehensive dataset for our research from multiple authoritative sources. Our dataset consisted of transcriptome data extracted from the Cancer Genome Atlas (TCGA) database(https://portal.gdc.cancer.gov/), encompassing a total of 555 specimens. This collection comprised 54 normal tissue samples and 501 samples of Lung Adenocarcinoma (LUAD) tissue. To enhance the comprehensiveness of our dataset, we integrated gene profiling data from normal lung tissues, sourced from the Genotypic Tissue Expression (GTEx) database, available at https://www.gtexportal.org/home/index.html. Additionally, we accessed the GSE211374 dataset from the GEO database(https://www.ncbi.nlm.nih.gov/geo/), meticulously curated by the National Center for Biotechnology Information. Furthermore, we incorporated LUAD proteomic data, obtained from the CPTAC data portal (https://cptac-data-portal.georgetown.edu/cptacPublic/). For the genomic profiles of various lung cancer cell lines, we extracted expression data from the CCLE database, accessible at https://sites.broadinstitute.org/ccle. Pharmacogenomic analysis using L1000CDS2 to explore potential therapeutic agents that could be used to treat lung adenocarcinoma the CDSDB database. Subsequent data analysis was executed using the R programming language，version 4.1.1. Data cleaning and ID conversion were performed with the tidyr, dplyr, and rtracklayer R packages.\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003e2.2. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis provides insights into higher-level functions of genes at the molecular level. We performed functional enrichment analysis of NRAS-related genes by using the cluster profile package in R software and visualized the results.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.3. Correlation analysis of NRAS expression with clinicopathologic features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of NRAS mRNA expression with clinicopathologic features was performed using the ggpubr software package. Box plots were employed to visually represent the associations between NRAS expression and relevant clinicopathological factors, encompassing tumor stage and survival.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.4.The analysis of tumor immunity estimation resource database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Tumor Immuno Estimation Resource (TIMER) database is a comprehensive online database. We used the TIMER algorithm to analyze the mutation frequencies of NRAS, HRAS and KRAS in each of the TCGA cancer types. To assess the abundance of four distinct immune infiltrates (B cells, CD4+ T cells, neutrophils, and macrophages), we employed the TIMER algorithm. Next, we determined the mRNA expression of NRAS in multiple human cancers. Finally, we used TIMER database to estimate the correlation between the expression of NRAS and abundance of several immune cells. Statistical significance was determined at a threshold of p \u0026lt; 0.05.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.5. Survival analyses of TCGA cancer data \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene expression profile and the clinical data of patients were downloaded from TCGA. It consisted of 501 tumor samples and 54 normal samples of patients with LUAD. Subsequently, we utilized R software and Strawberry Perl for data processing. Kaplan Meier plotter analysis was used to evaluate the impact of NRAS on the survival rate of LUAD patients.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.6. Inclusion criteria for LUAD puncture specimens prior to chemotherapy using paclitaxel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a classification criterion based on a combination of the RECIST (Response Evaluation Criteria in Solid Tumors) standards, the World Health Organization\u0026apos;s guidelines for clinical chemotherapy in lung cancer, and insights from practical clinical experience. This criterion was utilized to analyze patient data from Nantong University Hospital spanning 2021 to 2023, focusing on those who underwent three cycles of paclitaxel chemotherapy. The cohort comprised 13 patients demonstrating paclitaxel resistance and 11 patients exhibiting no resistance to the treatment. Our classification criteria included:\u003c/p\u003e\n\u003cp\u003e1) Radiologic Progression: Adhering to RECIST guidelines[17], we meticulously reviewed the imaging data of patients to observe changes in tumor size. Patients were categorized as non-resistant if their primary tumor foci either did not increase in size or did not manifest any new metastases post-chemotherapy. Metastases encompassed various forms, including intrapulmonary, brain, and bone metastases. Conversely, patients displaying enlargement in primary foci or emergence of new metastases were classified as resistant.\u003c/p\u003e\n\u003cp\u003e2) Biomarker Fluctuations: Evaluation of blood tumor markers (CEA, Cyfra21-1, etc) was another pivotal aspect of our classification. Patients whose tumor marker levels either remained stable or decreased following chemotherapy were assigned to the non-resistant group. In contrast, those exhibiting an increase in these biomarker levels were designated as resistant.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.7. Immunohistochemistry (IHC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLUAD tissue microarray (TMA) consisted of 338 LUAD specimens (Affiliated Hospital of Nantong University) for IHC analysis. Puncture specimen sections prior to chemotherapy with paclitaxel included 24 LUAD specimens for IHC analysis (Affiliated Hospital of Nantong University). IHC staining was performed using NRAS antibody (1:800, Proteintech). TMA sections and sections of pre-chemotherapy puncture specimens were manually scored by visual inspection by two independent researchers who kept clinicopathologic information confidential. Inconsistencies were resolved by discussion. The expression of NRAS was evaluated using the immune response score (IRS), which combines the scores for the percentage of positive cells (0-4: 0%; 1: 1\u0026sim;25%; 2: 26\u0026sim;50%; 3: 51\u0026sim;75%; 4: 76\u0026sim;100%) and the intensity of staining (0-3: 0: no staining is negative; 1: light yellow is weak; 2: brownish-yellow is intermediate; and 3: tan is strong).The total immunohistochemistry score was the percentage of positive tumour cells score multiplied by the staining intensity score. Low-expression groups were categorized according to a total IHC score of \u0026lt;6, and \u0026ge;6 were categorized as high-expression groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. GSEA and Single sample GSEA (ssGSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NRAS-associated genomic data in the TCGA database were categorized into positive and negative correlation groups based on risk scores. Gene Set Enrichment Analysis (GSEA) version 4.1.0 software (https://www.gsea-msigdborg/gsea/index.jsp) was utilized for enrichment analysis of hallmark genome and C6 genome. These groups were designated as phenotypes, and we set the number of permutations to 1000 while maintaining default values for all other options. Subsequently, we conducted single-sample Gene Set Enrichment Analysis (ssGSEA) using the GSVA and GSEA BaseR software packages.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.9. Cell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe H1299 and PC9 human non-small cell lung cancer cell lines were obtained from the Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, located in Shanghai, China. Cells were cultured in a medium supplemented with 10% fetal bovine serum (FBS) from Gibco (Billings, MT, USA), as well as 100 U/mL of penicillin and 100 \u0026micro;g/mL of streptomycin from Gibco (Carlsbad, CA, USA). Cell maintenance was carried out in a humidified incubator set at 37\u0026deg;C with a 5% CO2 atmosphere.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.10. Cell transfection and verification\u003c/strong\u003e\u003cbr\u003e We acquired NRAS-specific siRNAs from Gemma Genetics (Shanghai, China). The siRNA sequences synthesized were as follows:\u003c/p\u003e\n\u003cp\u003eNRAS-Homo-491:\u003c/p\u003e\n\u003cp\u003eSense: 5\u0026acute;-GCCAACAAGGACAGUUGAUTT-3\u0026acute;\u003cbr\u003e Antisense: 5\u0026acute;-AUCAACUGUCCUUGUGGCTT-3\u0026acute;\u003c/p\u003e\n\u003cp\u003eNRAS-Homo-530:\u003c/p\u003e\n\u003cp\u003eSense: 5\u0026acute;-GGCCAAGAGUUACGGGGAUUTT-3\u0026acute;\u003cbr\u003e Antisense: 5\u0026acute;-AAUCCCGUAACUCUUUGGCCTT-3\u0026acute;\u003c/p\u003e\n\u003cp\u003eNRAS-Homo-669:\u003c/p\u003e\n\u003cp\u003eSense: 5\u0026acute;-GGUUGUAUGGGAUUGCCAUTT-3\u0026acute;\u003cbr\u003e Antisense: 5\u0026acute;-AUGGCAAUCCCAUACAACCTT-3\u0026acute;\u003c/p\u003e\n\u003cp\u003eCells were transfected with these siRNA sequences utilizing the Lipofectamine 3000 Transfection Reagent (Invitrogen, USA), Follow the manufacturer\u0026apos;s recommendations. To assess the effectiveness of siRNA knockdown, RT-qPCR and Western blot were used to detect the gene knockout efficiency of NRAS. Based on their interference capabilities, NRAS-Homo-530 and NRAS-Homo-669 were selected for further functional cellular assays due to their superior interference efficiency.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.11. RT-qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was extracted from lung tissue with triazole reagent (Invitgen) following the established protocol. Subsequently, complementary DNA (cDNA) synthesis was carried out in accordance with the manufacturer\u0026apos;s instructions for the Hifair II 1st Strand cDNA Synthesis SuperMix (11120ES60; Yeasen, Shanghai, China). The synthesized cDNA samples were then subjected to quantitative Real-time Polymerase Chain Reaction (RT-qPCR) analysis utilizing Hieff qPCR SYBR Green Master Mix (Low Rox Plus) (11202ES08; Yeasen, Shanghai, China). This workflow ensured the accurate and reliable assessment of gene expression levels. For each sample, 2\u0026mu;g of RNA was reverse transcribed and the qPCR reaction system was 10\u0026mu;l, of which 2\u0026mu;l of cDNA was used. The fold changes were calculated according to the formula 2\u0026minus;\u0026Delta;\u0026Delta;Ct method. In our work, \u0026beta;-actin was used as the internal control.\u003c/p\u003e\n\u003cp\u003eThe primers used were as follows:\u003c/p\u003e\n\u003cp\u003eNRAS:\u003c/p\u003e\n\u003cp\u003eForward: CTGGGTTCTTCCACAGCACA\u003cbr\u003e Reverse: TTCACGTTTGCGGTTTGGTT\u003c/p\u003e\n\u003cp\u003eBIRC2:\u003c/p\u003e\n\u003cp\u003eForward: AGCACGATCTTGTCAGATTGG\u003cbr\u003e Reverse: GGCGGGGAAAGTTGAATATGTA\u003c/p\u003e\n\u003cp\u003eMSH6:\u003c/p\u003e\n\u003cp\u003eForward: GCAATGCAACGTGCAGATGAA\u003c/p\u003e\n\u003cp\u003eReverse: ACTTCGCCTAGATCCTTGTGT\u003c/p\u003e\n\u003cp\u003eMSH2:\u003c/p\u003e\n\u003cp\u003eForward: AGGCATCCAAGGAGAATGATTG\u003cbr\u003e Reverse: GGAATCCACATACCCAACTCCAA\u003c/p\u003e\n\u003cp\u003eTOP2A:\u003c/p\u003e\n\u003cp\u003eForward: ACCATTGCAGCCTGTAAATGA\u003cbr\u003e Reverse: GGGCGGAGCAAAATATGTTCC\u003c/p\u003e\n\u003cp\u003eBRCA1:\u003c/p\u003e\n\u003cp\u003eForward: TTGTTACAAATCACCCCTCAAGG\u003cbr\u003e Reverse: CCCTGATACTTTTCTGGATGCC\u003c/p\u003e\n\u003cp\u003ePIK3CA:\u003c/p\u003e\n\u003cp\u003eForward: AGTAGGCAACCGTGAAGAAAAG\u003cbr\u003e Reverse: GAGGTGAATTGAGGTCCCTAAGA\u003c/p\u003e\n\u003cp\u003eAPAF1:\u003c/p\u003e\n\u003cp\u003eForward: GTCACCATACATGGAATGGCA\u003cbr\u003e Reverse: CTGATCCAACCGTGTGCAAA\u003c/p\u003e\n\u003cp\u003eBIRC5:\u003c/p\u003e\n\u003cp\u003eForward: AGGACCACCGCATCTCTACAT\u003cbr\u003e Reverse: AAGTCTGGCTCGTTCTCAGTG\u003c/p\u003e\n\u003cp\u003eSLC31A1:\u003c/p\u003e\n\u003cp\u003eForward: GGGGATGAGCTATATGGACTCC\u003cbr\u003e Reverse: TCACCAAACCGGAAAACAGTAG\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin:\u003c/p\u003e\n\u003cp\u003eForward: AGTTGCGTTACACCCTTTCTTG\u003cbr\u003e Reverse: GCTGTCACCTTCACCGTTCC\u003c/p\u003e\n\u003cp\u003eThe above primers were purchased from Bioengineering (Shanghai) Co.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12. Western blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh tissue samples were homogenized using homogenizers in a protein lysis buffer containing protease inhibitors to ensure thorough protein extraction. The protein concentrations were quantified using the bicinchoninic acid method. Subsequently, the proteins were electrophoresed on a 12% sodium dodecyl sulfate polyacrylamide gel (SDS-PAGE) (cat. #XP00100BOX; Thermo, USA). Following electrophoresis, the proteins were transferred onto polyvinylidene difluoride (PVDF) membranes (cat. #88518; Thermo). To prevent non-specific binding, the membranes were blocked with 5% skim milk in Tris-Buffered Saline containing Tween-20 (TBS-T) and then incubated with primary antibodies overnight at 4\u0026deg;C. Primary antibodies included rabbit anti-NRAS (dilution 1:2000; cat. 10724-1-AP; ProteinTech) and mouse anti-\u0026beta;-actin (dilution 1:25000; cat. 66009-1-lg; ProteinTech). After primary antibody incubation, incubate the membrane with secondary antibody for 1-hour, secondary antibody including Goat Anti-Rabbit IgG (dilution 1:8000; cat. SA00001-2; ProteinTech) and Goat Anti-Mouse IgG (dilution 1:8000; cat. SA00001-1; ProteinTech). Protein bands were visualized using the enhanced chemiluminescence technique. Band densities were quantified with Image J software (National Institutes of Health, Bethesda, MD) and subsequently normalized to \u0026beta;-actin. Relative protein levels were calculated as the density ratios of the target protein to \u0026beta;-actin.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.13. Cell viability assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH1299 cells and PC9 cells, including unmodified cells and those transfected with NRAS-Homo-530 and NRAS-Homo-669, were seeded in 96-well culture plates at a density of 1500 cells per well. After a 24-hour incubation period, the medium was refreshed with new medium supplemented with varying concentrations of paclitaxel (30 mg/5 ml, Nanjing Luye Pharmaceutical Co., Ltd): 0 \u0026micro;g/ml, 0.003 \u0026micro;g/ml, 0.03 \u0026micro;g/ml, 0.3 \u0026micro;g/ml, 30 \u0026micro;g/ml, and 300 \u0026micro;g/ml. This medium, containing the respective paclitaxel concentrations, was renewed every 48 hours. On the seventh day post-inoculation, cell viability was assessed. CCK-8 reagent (C0037, Limited Company., Shanghai, China) was added to each well and the plates were further incubated for 2 hours in a 5% CO2 humidified atmosphere. Subsequently, the absorbance at 450 nm was measured using a spectrophotometric enzyme assay reader. Each condition had three replicate wells, and the mean absorbance value was computed for analysis. The half maximal inhibitory concentration (IC50) for both the NRAS knockdown and control cells was deduced by comparing cell viability percentages against those of untreated controls.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.14. Wound healing assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cells in the H1299 NC group, NRAS-Homo-530 interference group, and NRAS-Homo-669 interference group were each placed into separate 6-well culture plates, with a cell density of 5\u0026times;10^5 cells per well, and PC9 cells were similarly treated. Subsequently, they were cultured for a duration of 24 hours at 37\u0026deg;C in an environment with 5% CO2. Following incubation, the medium was aspirated, and a uniform scratch was created in the cell monolayer using a 10-\u0026micro;l pipette tip. Cells were then gently rinsed twice with PBS to remove any detached cells. Subsequently,2 ml of RPMI 1640 medium was added to each well. Scratch wound images were captured at time points of 0, 24 and 48 hours post-scratching. Each experimental condition had three replicate wells. The migration distance of the cells into the scratched region was quantified over the designated time periods. The resultant data were normalized and presented as a migration index, calculated as the ratio of the migration distance of the experimental groups to that of the NC group.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2.15. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing R programming language (version 4.1.1) for statistical analysis, GraphPad Prism Software (version 9.0, Graph Pad Software Inc., La Jolla, CA, USA) and SPSS (version 26.0, SPSS, Inc., Chicago, IL, USA). Pearson\u0026apos;s correlation coefficients were computed with case weighting to derive P-values. T-tests were utilized to compare two groups, whereas one-way analysis of variance (ANOVA) was employed for comparisons involving multiple groups, with the use of chi-square testing when appropriate. For post-hoc analyses of significant ANOVA results, Pairwise comparison using Q test in instances of non-significance, the non-parametric rank sum test was adopted. Survival data were depicted using the Kaplan-Meier method, and statistical differences between the curves were assessed using the log-rank test. A significance threshold of P \u0026lt; 0.05 was applied to all statistical analyses.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Paclitaxel resistance is associated with the RAS signaling pathway, and NRAS is a high-quality target for the treatment of lung adenocarcinoma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our investigation of paclitaxel resistance in lung adenocarcinoma (LUAD), we utilized the Genome Expression Omnibus (GEO) database to identify relevant datasets. Our search yielded dataset GSE211374 as a potential source of insight (available at https://www.ncbi.nlm.nih.gov/geo/). To ensure the reliability of this dataset, we recalculated the raw data using both the server\u0026rsquo;s resources and the Linear Models for Microarray Data (LIMMA) pipeline, depicting our findings in Supplementary Figure 1A-E. We performed a differential expression analysis comparing paclitaxel-resistant and non-resistant cohorts within GSE211374 to identify genes exhibiting significant expression differences. Subsequently, the identified genes underwent pathway enrichment analysis via the Kyoto Encyclopedia of Genes and Genomes (KEGG) to elucidate their associated pathways. Our analysis revealed a significant enrichment of these genes across several pathways, including the RAS signaling pathway, T cell receptor signaling pathway, and PI3K-Akt signaling pathway. Figure 2A presents this pronounced enrichment, particularly highlighting that of the RAS signaling pathway. This enrichment prompted our hypothesis that activating the RAS signaling pathway potentially contributes to the development of paclitaxel resistance in LUAD.\u003c/p\u003e\n\u003cp\u003eWithin the RAS gene family encompassing KRAS, NRAS, and HRAS, we explored their correlation with the clinical characteristics of LUAD using the Cancer Genome Atlas (TCGA) database (accessible at https://www.cancer.gov/ccg/research/genome-sequencing/tcga). Our exploration revealed that NRAS exhibited higher expression levels than KRAS and HRAS and showed an increase in expression aligning with the advancement of clinical stages (Figures 2B and C). Moreover, elevated NRAS expression correlated with a poorer prognosis (Figure 2D). In the analysis of mutation frequencies across NRAS, KRAS, and HRAS in pan-cancer within the TIMER2.0 database, we found that the mutation frequencies of NRAS were lower than that of KRAS and HRAS in LUAD (Figure 2E, Supplementary figures 2A and B). Importantly, the presence or absence of a mutation in NRAS did not significantly correlate with the survival prognosis of patients (Supplementary\u0026nbsp;figure 2C). We also generated proteomic correlation heat maps of NRAS protein expression, associating NRAS protein expression with clinicopathological features utilizing the CPTAC database (Figure 2F). In summary, our data strongly suggested that NRAS as a high-quality research target in LUAD, especially in the context of paclitaxel resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. NRAS is highly up-regulated in LUAD and correlates with poor clinical outcomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the prognostic implications and potential therapeutic significance of NRAS in lung adenocarcinoma (LUAD), we conducted an extensive analysis of NRAS expression using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project datasets. Our analyses revealed significant overexpression of NRAS in LUAD tissue compared to adjacent non-tumorous tissue, a finding that persisted in both paired and unpaired analyses (Figure 3A-C). Further evaluation of NRAS as a prognostic and diagnostic marker was conducted via Receiver Operating Characteristic (ROC) analysis, indicating a robust predictive accuracy for LUAD, as evidenced by an area under the ROC curve (AUC) of 0.793 (Figure 3D). In addition, by Kaplan-Meyer survival curve analysis, we divided the patients into NRAS high expression group (the largest 25%) and low expression group (the smallest 75%) according to the upper quartile, and into NRAS high expression group (the largest 75%) and low expression group (the smallest 25%) according to the lower quartile. The results all showed that high NRAS expression was associated with a significant reduction in overall survival (OS) in LUAD patients (Figure 3E and F).\u003c/p\u003e\n\u003cp\u003eMoreover, our analyses of overall survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI) consistently indicated that elevated NRAS expression was associated with adverse outcomes (Supplementary Tables 1-3). To confirm our bioinformatic findings, we conducted immunohistochemical (IHC) staining on cancer specimens. The IHC results aligned with our computational findings, demonstrating predominant cytoplasmic localization of NRAS in LUAD cells and significantly higher expression levels in tumor than adjacent non-neoplastic tissue (Figure 3G). Using lung adenocarcinoma tissue microarray (TMA), we also observed correlations between NRAS gene expression and vascular thrombus, tumor size, degree of differentiation, T-stage, N-stage and overall survival. However, we noted comparatively weaker associations with other clinicopathological parameters (Table 1). These comprehensive findings affirm the prognostic significance of NRAS expression in LUAD and suggest its potential utility in guiding therapeutic decisions. This highlights the necessity for further research into NRAS as a target for intervention in LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Enrichment analysis of KEGG pathway related to NRAS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo unravel the underlying mechanism of paclitaxel resistance mediated by NRAS in lung adenocarcinoma (LUAD) cells, we completed an analysis targeting NRAS-related genes. Initially, heat maps were generated to visualize the correlation between NRAS-related genes and survival status and clinical stage using the TCGA database (Figure 4A). We then subjected these NRAS-related genes to Gene Set Enrichment Analysis (GSEA), revealing their significant abundance in key pathways, notably the G2/M checkpoint and vascular endothelial growth factor (VEGF) signaling pathway (Figure 4B and C). To delve deeper into the mechanistic aspects, we analyzed differential expression to identify genes that exhibited variation between high and low NRAS expression cohorts (Figure 4D). Interacting these differentially expressed genes with those strongly associated with NRAS led to the identification of a subset comprising 97 genes (Figure 4E). Subsequently, we constructed a Protein-Protein Interaction (PPI) network for these 97 genes, revealing a dense web of interaction, suggesting a complex regulatory network (Figure 4F). Further KEGG enrichment analysis of this gene network highlighted their significant involvement in essential cellular pathways, including those regulating the cell cycle and DNA damage repair mechanisms (Figure 4G and H). In conclusion, our comprehensive analysis of NRAS-related genes in LUAD has shed light on the importance of the G2/M checkpoint and VEGF signaling pathways. The identified gene network and its association with critical cellular processes provide a foundation for future targeted therapies aimed at overcoming resistance in lung adenocarcinoma treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. NRAS related immune infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the correlation between several immune cells and NRAS utilizing the TIMER2.0 database. Our analysis revealed a significant negative correlation with B cells and CD4+ T cells while demonstrating a significant positive correlation with neutrophils and macrophages (Figure 5A-D). We then conducted ssGSEA enrichment analysis of NRAS with 24 types of immune cells within the TCGA database. This analysis showed that the high and low expression groups of NRAS exhibited a significant correlation with several immune cells, including B cells, CD8 T cells, eosinophils, macrophages, mast cells, neutrophils, CD56 bright NK cells, NK cells, Plasmacytoid DC, T Helper cells, T cells, TFH, Tgd, Th17 cells, and Th2 cells (Figure 5E). Further, ssGSEA enrichment analysis revealed a significant correlation between NRAS and various immune cell types, particularly Th2 cells, p DC, Th17 cells, \u0026nbsp; CD56 bright NK cells, CD8 T cells, and T follicular helper \u0026nbsp;cells (Figure 5F). This evidence indicates that targeting NRAS could hold promise for immunotherapeutic interventions in managing LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Knockdown of NRAS increases the sensitivity of LUAD to paclitaxel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we analyzed NRAS expression across four lung adenocarcinoma cell lines within the TCGA database, highlighting higher NRAS expression in the H1299 and PC9 cell lines (Figure 6A). To corroborate our findings, a series of laboratory experiments was conducted. We employed three distinct small interfering RNAs (siRNAs) to achieve NRAS knockdown in the H1299 and PC9 lung adenocarcinoma cell lines. The efficacy of NRAS silencing was confirmed through quantitative polymerase chain reaction (qPCR) and Western blot (WB) analyses (Figures 6B-E). Among the tested siRNAs, SI-530 and SI-669 exhibited potent knockdown efficiency, prompting their selection for subsequent experiments. We then introduced paclitaxel to the control and NRAS-silenced groups in H1299 and PC9 cell lines. Post-treatment, we utilized the CCK8 assay to assess cellular viability. Strikingly, the NRAS-silenced group exhibited a significant reduction in the half-maximal inhibitory concentration (IC50) of paclitaxel (P\u0026lt;0.01), suggesting that inhibiting NRAS potentiates the cytostatic effects of paclitaxel in both cell lines (Figure 6F and G). Additionally, wound healing assays were conducted to evaluate cell migration. Notably, NRAS knockdown significantly inhibited cell migration at 24 and 48 hours post-treatment in both cell lines (Figure 6H and I). These findings underscore the role of NRAS in modulating the responsiveness of LUAD cells to paclitaxel\u0026rsquo;s anti-migratory effects in both cell lines. Further investigation involved analyzing lung needle biopsies from 24 patients with lung adenocarcinoma prior to paclitaxel chemotherapy. Immunohistochemical staining revealed markedly lower NRAS expression in tumor tissues from the non-resistant group compared to the drug-resistant group (Figure 6J). Other clinical characteristics of the patients are shown in Table\u0026nbsp;2. These comprehensive analyses provide compelling evidence that NRAS significantly influences paclitaxel resistance in LUAD. Furthermore, we performed pharmacogenomic analyses using L1000CDS2 in the CDSDB database to predict potential therapeutic agents for the treatment of lung adenocarcinoma (Figure 6K). The drug prediction outcomes present several potential therapeutic options for LUAD, which warrant further exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6. Strong correlation between NRAS and genes related to paclitaxel resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur investigative approach took a multifaceted trajectory to elucidate further the relationship between NRAS and paclitaxel resistance in lung adenocarcinoma (LUAD). Initially, we queried the TCGA database to identify NRAS-related genes. Subsequently, we accessed the PharmaGkb database (https://www.pharmgkb.org/) to retrieve relevant gene sets associated with therapy drugs for LUAD. Conducting Gene Set Enrichment Analysis (GSEA) on these datasets indicated significant enrichment of NRAS-related genes within the Pa450761 gene set, which is linked to paclitaxel resistance. This substantiated the correlation between NRAS and paclitaxel resistance in LUAD (Figures 7A and B). Our research further identified nine genes strongly associated with NRAS within the context of paclitaxel resistance in LUAD (Figure 7C). To validate these findings, we performed RT-qPCR analyses to assess the expression levels of these genes post-NRAS knockdown. Remarkably, BIRC2, MSH6, MSH2, TOP2A, and BRCA1 expression exhibited concurrent downregulation with NRAS (Figure 7D-H), affirming their correlation. However, PIK3CA, APAF1, BIRC5, and SLC31A1 did not correlate significantly with NRAS expression (Supplementary Figure 3A-D). This provided further insight into the NRAS-related molecular network associated with paclitaxel resistance. These findings underscore the potential of NRAS as a therapeutic target to enhance paclitaxel\u0026apos;s efficacy in treating lung adenocarcinoma.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLung adenocarcinoma stands out as one of the most prevalent cancer types, affecting a substantial number of patients at intermediate to advanced disease stages[18, 19]. Paclitaxel, a cornerstone chemotherapeutic drug, continues to play a pivotal role in the treatment of lung adenocarcinoma. However, a concerning trend of increasing resistance to paclitaxel has emerged in recent observations, with many patients exhibiting varying levels of drug tolerance[20]. This study presents a comprehensive analysis of NRAS expression and its impact on paclitaxel resistance in lung adenocarcinoma (LUAD). Our findings corroborate previous assertions of NRAS as a significant oncogenic driver and contribute to advancing the current understanding of its role in chemoresistance, offering insights that could steer future therapeutic strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our study, we conducted pathway enrichment analysis utilizing bioinformatics approaches on patients undergoing paclitaxel chemotherapy. This analysis revealed a critical role of the RAS signaling pathway in the development of paclitaxel resistance. The RAS signaling pathway is vital in various cellular functions, such as growth, division, survival, and migration[21]. Within the RAS protein family, comprising KRAS, NRAS, and HRAS, the mutation frequency of KRAS in lung adenocarcinoma can be as high as 32%. Developing therapies targeting KRAS poses a significant obstacle due to the absence of drug-binding sites, rendering it a complex and challenging therapeutic target. Interestingly, KRAS mutations are more common in lung adenocarcinoma than in other adenocarcinomas, whereas NRAS mutations are less common in lung adenocarcinoma. While a large body of literature exists on NRAS-mutant melanoma and its association with poor disease prognosis, especially in advanced stages, therapeutic targets often involve combined inhibition of MAPK signaling and CDK4/6-driven cell cycle progression[22]. However, the role of NRAS in lung adenocarcinoma remains relatively under-investigated.\u003c/p\u003e\n\u003cp\u003eOur findings, derived from a fusion of TCGA and GTEx datasets, reveal a significant upregulation of NRAS in tumor tissues compared to adjacent non-tumorous counterparts. This observation is consistent with previous research emphasizing NRAS\u0026rsquo;s oncogenic role across multiple cancer types[23]. The consistent increase in expression observed across multiple independent datasets highlights NRAS\u0026rsquo;s potential as a biomarker for LUAD, further confirming its diagnostic value, as indicated by the area under the ROC curve of 0.793. This diagnostic potential is particularly promising, considering the challenges associated with early detection of LUAD. Moreover, immunohistochemical analyses further substantiate these findings, revealing a predominant cytoplasmic NRAS localization pattern consistent with the protein\u0026rsquo;s involvement in signal transduction pathways. Our immunohistochemical analysis suggests that aberrant NRAS expression may affect multiple downstream pathways. This finding underscores\u0026nbsp;the significance of NRAS\u0026rsquo;s subcellular distribution in its interactions with various signaling molecules, consequently impacting cell proliferation and survival. The Kaplan-Meier survival curves illustrate the prognostic implications of NRAS expression. Our study highlights that patients exhibiting elevated NRAS levels experienced significantly poorer overall survival rates. Such data indicate the aggressive nature of NRAS-driven tumors, emphasizing the urgent need for targeted interventions in this context.\u003c/p\u003e\n\u003cp\u003eUpon dissecting pathways related to NRAS, our enrichment analysis highlighted the involvement of the G2/M checkpoint and VEGF signaling pathways. The G2/M checkpoint is crucial for maintaining genomic integrity, and its disruption is a recognized hallmark of cancer[24]. Meanwhile, the VEGF pathway\u0026rsquo;s role in angiogenesis and its implications in tumor progression and metastasis have been well-documented[25, 26]. The enrichment observed in the G2/M checkpoint and VEGF signaling pathways is noteworthy, suggesting that NRAS may contribute to LUAD pathogenesis through multiple pathways, potentially involving the promotion of angiogenesis and facilitation of cell cycle dysregulation. In unraveling these mechanisms, our gene correlation analysis and subsequent KEGG pathway enrichment revealed a strong association of NRAS-related genes with pathways critical for cell cycle progression and DNA repair. Enriching NRAS-related genes within these pathways establishes a mechanistic link between NRAS overexpression and the recognized hallmarks of cancer. Notably, our pathway enrichment analysis indicated potential mediation of paclitaxel resistance through these pathways. The aberrant regulation of these pathways can lead to unchecked cellular proliferation and a failure to rectify DNA damage, cultivating an environment conducive to the development and progression of cancer. The dysregulation of these pathways has been implicated in resistance to various chemotherapeutic agents, including paclitaxel[27, 28]. Our experiments involving NRAS knockdown displayed a concurrent decrease in genes associated with paclitaxel resistance, consequently enhancing paclitaxel\u0026rsquo;s inhibitory impact on lung adenocarcinoma cell proliferation and migration. These findings underscore the potential of NRAS targeting to augment paclitaxel sensitivity in lung adenocarcinoma cells.\u003c/p\u003e\n\u003cp\u003eThe protein-protein interaction network further elucidated the complexity of interactions governed by NRAS, indicating its potential role as a central point in the oncogenic signaling network. The convergence of differentially expressed genes with NRAS-associated genes yielded a set of 97 genes, forming a densely connected network. This network\u0026rsquo;s complexity typifies a robust biological system capable of sustaining oncogenic signaling pathways and potentially resisting therapeutic interventions. Such robustness may offer a plausible explanation for the development of paclitaxel resistance in LUAD.\u003c/p\u003e\n\u003cp\u003eOur study\u0026rsquo;s strength lies in the integration of multi-omic data and the validation of findings through both in silico and immunohistochemical analyses. However, we do acknowledge certain limitations inherent to our study design. The study\u0026apos;s retrospective nature and reliance on public databases may introduce certain inherent biases. Thus, prospective studies are crucial to validate our findings and delve deeper into the therapeutic targeting potential of NRAS and its associated gene network. It\u0026rsquo;s also important to note that our investigations were primarily conducted at the cellular level. Future experiments should incorporate animal models to simulate in vivo conditions more accurately. Additionally, our exploration focused on a subset of NRAS functionalities, leaving several potential roles in lung adenocarcinoma unexplored. Future studies should encompass a broader investigation of NRAS functions in lung adenocarcinoma.\u003c/p\u003e\n"},{"header":"5. Conclusion","content":"\u003cp\u003eour research delineates a clear association between NRAS overexpression and paclitaxel resistance in LUAD, providing pivotal preliminary insights into NRAS\u0026rsquo;s involvement in lung adenocarcinoma. It elucidates the association between NRAS and paclitaxel resistance, hinting at potential drug interventions that could enhance paclitaxel\u0026rsquo;s efficacy. This study offers novel therapeutic possibilities for patients resistant to paclitaxel and paves the way for subsequent investigative endeavors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLUAD, Lung adenocarcinoma; NSCLC, non-small cell lung cancer; MTOC, microtubule organization center; TCGA, The Cancer Genome Atlas; GTEx, Genotypic Tissue Expression; KEGG, Kyoto Encyclopedia of Genes and Genomes; TIMER, Tumor Immuno Estimation Resource; RECIST ,Response Evaluation Criteria in Solid Tumors; IHC, immunohistochemistry; IRS, immune response score; GSEA; Gene Set Enrichment Analysis; ssGSEA, Single sample GSEA; FBS, fetal bovine serum; cDNA, complementary DNA;RT-qPCR, Reverse Transcription-Polymerase Chain Reaction ;WB, Western Blot; PVDF, polyvinylidene difluoride; ROC, Receiver Operating Characteristic; OS, overall survival; DSS, Disease-Specific Survival; PFI, Progression-Free Interval; TMA, tissue microarray; VEGF, vascular endothelial growth factor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available within the article/Supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis protocol\u0026apos;s design adheres to the Declaration of Helsinki principles and received approval from the Ethics Committee of Affiliated Hospital of Nantong University (reference number 2022-L078).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available at the database URLs mentioned in the material and methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Taoming Mo, Shuang Zhang, Qishuang Wei, Yifei Liu. Methodology and Investigation: Taoming Mo, Shuang Zhang, Qishuang Wei, Yali Zhang. Visualization: Taoming Mo, Shuang Zhang, Li Tong, Sichu Wang, Lijuan Tang. Funding acquisition: Taoming Mo, Tingting Bian, Jianguo Zhang, Jun Zhu, Yifei Liu. Project administration and Supervision: Tingting Bian, Yifei Liu, Shaolei Lu. Writing \u0026ndash; original draft: Shuang Zhang, Taoming Mo. Writing \u0026ndash; review \u0026amp; editing: Taoming Mo, Shuang Zhang, Ryan Liu, Shaolei Lu, Yifei Liu. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from National Natural Science Foundation of China (No. 82273422), Postgraduate Research Project and Practice Innovation Program of Jiangsu province (No. SJCX22_1639),2022 Nantong Basic Research Plan Project (JC12022016, JC22022020, MS22022017) and 2023 Nantong Social Livelihood Science and Technology Plan Project (MS2023067).\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\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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 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indicator AHNAK2 and immune infiltrates in lung adenocarcinoma\u003c/strong\u003e. \u003cem\u003eInt Immunopharmacol \u003c/em\u003e2021, \u003cstrong\u003e90\u003c/strong\u003e:107134.\u003c/li\u003e\n\u003cli\u003eWang W, Wang J, Liu S, Ren Y, Wang J, Liu S, Cui W, Jia L, Tang X, Yang J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAn EHMT2/NFYA-ALDH2 signaling axis modulates the RAF pathway to regulate paclitaxel resistance in lung cancer\u003c/strong\u003e. \u003cem\u003eMol Cancer \u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):106.\u003c/li\u003e\n\u003cli\u003eRitt DA, Abreu-Blanco MT, Bindu L, Durrant DE, Zhou M, Specht SI, Stephen AG, Holderfield M, Morrison DK: \u003cstrong\u003eInhibition of Ras/Raf/MEK/ERK Pathway Signaling by a Stress-Induced Phospho-Regulatory Circuit\u003c/strong\u003e. \u003cem\u003eMol Cell \u003c/em\u003e2016, \u003cstrong\u003e64\u003c/strong\u003e(5):875-887.\u003c/li\u003e\n\u003cli\u003eRandic T, Kozar I, Margue C, Utikal J, Kreis S: \u003cstrong\u003eNRAS mutant melanoma: Towards better therapies\u003c/strong\u003e. \u003cem\u003eCancer Treat Rev \u003c/em\u003e2021, \u003cstrong\u003e99\u003c/strong\u003e:102238.\u003c/li\u003e\n\u003cli\u003eAbravanel DL, Nishino M, Sholl LM, Ambrogio C, Awad MM: \u003cstrong\u003eAn Acquired NRAS Q61K Mutation in BRAF V600E-Mutant Lung Adenocarcinoma Resistant to Dabrafenib Plus Trametinib\u003c/strong\u003e. \u003cem\u003eJ Thorac Oncol \u003c/em\u003e2018, \u003cstrong\u003e13\u003c/strong\u003e(8):e131-e133.\u003c/li\u003e\n\u003cli\u003eMuller I, Strozyk E, Schindler S, Beissert S, Oo HZ, Sauter T, Lucarelli P, Raeth S, Hausser A, Al Nakouzi N\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCancer Cells Employ Nuclear Caspase-8 to Overcome the p53-Dependent G2/M Checkpoint through Cleavage of USP28\u003c/strong\u003e. \u003cem\u003eMol Cell \u003c/em\u003e2020, \u003cstrong\u003e77\u003c/strong\u003e(5):970-984 e977.\u003c/li\u003e\n\u003cli\u003eZhao Y, Guo S, Deng J, Shen J, Du F, Wu X, Chen Y, Li M, Chen M, Li X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eVEGF/VEGFR-Targeted Therapy and Immunotherapy in Non-small Cell Lung Cancer: Targeting the Tumor Microenvironment\u003c/strong\u003e. \u003cem\u003eInt J Biol Sci \u003c/em\u003e2022, \u003cstrong\u003e18\u003c/strong\u003e(9):3845-3858.\u003c/li\u003e\n\u003cli\u003ePatel SA, Nilsson MB, Le X, Cascone T, Jain RK, Heymach JV: \u003cstrong\u003eMolecular Mechanisms and Future Implications of VEGF/VEGFR in Cancer Therapy\u003c/strong\u003e. \u003cem\u003eClin Cancer Res \u003c/em\u003e2023, \u003cstrong\u003e29\u003c/strong\u003e(1):30-39.\u003c/li\u003e\n\u003cli\u003eDatta S, Choudhury D, Das A, Mukherjee DD, Dasgupta M, Bandopadhyay S, Chakrabarti G: \u003cstrong\u003eAutophagy inhibition with chloroquine reverts paclitaxel resistance and attenuates metastatic potential in human nonsmall lung adenocarcinoma A549 cells via ROS mediated modulation of \u0026beta;-catenin pathway\u003c/strong\u003e. \u003cem\u003eApoptosis \u003c/em\u003e2019, \u003cstrong\u003e24\u003c/strong\u003e(5-6):414-433.\u003c/li\u003e\n\u003cli\u003eLi B, Gu W, Zhu X: \u003cstrong\u003eNEAT1 mediates paclitaxel-resistance of non-small cell of lung cancer through activation of Akt/mTOR signalling pathway\u003c/strong\u003e. \u003cem\u003eJ Drug Target \u003c/em\u003e2019, \u003cstrong\u003e27\u003c/strong\u003e(10):1061-1067.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Relationship between NRAS expression and pathological parameters of LUAD TMA.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"765\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical pathology parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRAS low expression group\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=167\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRAS high expression group\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003en=171\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eX\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eyears\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e2.7481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e0.09737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e1.0368\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e0.3086\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVascular Tumor Embolus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e22.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.54E-06\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003eAirway Dissemination\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e0.67532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e0.4112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003ePleural Involvement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e1.4053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e0.2358\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor Size(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e15.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.34E-05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegree of Differentiation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Well\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePoor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e45.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.62E-10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; T4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e17.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.23E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;N0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;N1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;N2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e27.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.04E-06\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; M0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; M1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e333\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e3.1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e0.07578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ⅰ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ⅱ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ⅲ+Ⅳ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e48.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.73E-11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.705882352941178%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Survival\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.594771241830065%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.73856209150327%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.287581699346406%\" valign=\"top\"\u003e\n \u003cp\u003e12.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.934640522875817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.21E-04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 Association of NRAS expression with blood tumor markers and radiological progression in LUAD pre-chemotherapy puncture specimens.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.14745308310992%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3002680965147455%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eyear\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3002680965147455%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.975871313672926%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological Progression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.546916890080429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.08847184986595%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChanges in Blood Tumor Markers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIHC Scoring\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.04781704781705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-treatment Tumor Size\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.04781704781705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-treatment Tumor Size\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.04781704781705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastatic Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.95010395010395%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.226611226611226%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-treatment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;CEA\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eng/ml\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.226611226611226%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-treatment\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCEA\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eng/ml\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.226611226611226%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-treatment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cyfra21-1\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eng/ml\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.226611226611226%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-treatment\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cyfra21-1\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eng/ml\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.14745308310992%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaclitaxel - Sensitive Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3002680965147455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.3002680965147455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.975871313672926%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.546916890080429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.08847184986595%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.640750670241287%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5*2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.9*2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7*6.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4*3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e137.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e38.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.6*2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.2*2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e49.98\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.7*2.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8*1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.1*1.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.4*1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e377.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e218.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n 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\u003cp\u003e\u003cstrong\u003e2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e78\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8*2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.0*1.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.6*2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.9*2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e235.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e73\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.4*3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6*2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e25.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.3*1.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.2*0.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5*2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.5*2.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e496.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaclitaxel -Resistant Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6*1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.3*2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph Node Metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.651006711409396%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.167785234899329%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e71\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.3*3.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5*3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePleural Metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.308724832214765%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.9*5.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.9*5.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatic Metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.8*2.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.9*3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.006711409395972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph Node Metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.5503355704697985%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.248322147651007%\" valign=\"top\"\u003e\n 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LUAD","lastPublishedDoi":"10.21203/rs.3.rs-4306414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4306414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Lung adenocarcinoma (LUAD)poses substantial therapeutic complexities due to its resistance to first-line chemotherapic agents such as paclitaxel. This study investigated the involvement of NRAS in the development of paclitaxel resistance in LUAD. We integrated transcriptome data from TCGA and other databases while conducting in vitro experiments We also used GSEA to identify NRAS-related genes and pathways. Our findings revealed a significant up-regulation of NRAS in LUAD tissue, with higher NRAS expression correlating with adverse patient outcomes and decreased sensitivity to paclitaxel. Pathway enrichment analysis further revealed that NRAS-related genes significantly contributed to cell cycle dysregulation and impaired DNA damage repair mechanisms. Additionally, our experiments demonstrated that NRAS knockdown in LUAD cell lines exhibited increased sensitivity to paclitaxel, suggesting its potential as a viable therapeutic target. Targeting NRAS has the potential to enhance the efficacy of paclitaxel treatment in LUAD patients, offering a hopeful avenue for enhancing patient prognosis.","manuscriptTitle":"Targeting NRAS Inhibits Cancer Cell Growth and Enhances Paclitaxel Sensitivity in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 19:17:37","doi":"10.21203/rs.3.rs-4306414/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":"3e36db7c-3e97-47dd-8150-1b19698bd861","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-19T02:36:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 19:17:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4306414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4306414","identity":"rs-4306414","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}