Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics

Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics | 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 Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics Binbin Hu, Xiangwei Xia, Changfeng Man This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7767452/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 Objective: This study aims to investigate the role of lncRNAs in lung adenocarcinoma using bioinformatics methods. Methods: Gene expression profiles and clinical data of lung adenocarcinoma patients were extracted from the TCGA database. PERL software was used to distinguish between mRNA and lncRNA, and edgeR software was employed to identify differentially expressed lncRNAs. Univariate Cox regression was then applied to further screen for differentially expressed lncRNAs associated with prognosis. A predictive model was constructed using Lasso regression, followed by the generation of risk scores to distinguish between high-risk and low-risk groups. ROC curves were generated to visualize the predictive ability of the current features for the disease. Multivariate Cox regression analysis was used to evaluate risk genes. Results: We identified 119 differentially expressed lncRNAs, of which 97 were upregulated and 22 were downregulated. Seventeen differentially expressed lncRNAs were associated with survival prognosis. We further investigated the role of these prognostic differentially expressed lncRNAs in lung adenocarcinoma. The prognostic differentially expressed lncRNAs were divided into two subgroups, and it was found that the survival of cluster 1 was better than that of cluster 2. A prognostic model was then established, and the results indicated eight risk lncRNAs. Finally, multivariate Cox regression analysis revealed that three risk lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—could serve as independent prognostic factors and play important roles in lung adenocarcinoma. Conclusion: Using bioinformatics methods, we preliminarily explored the role of lncRNAs in lung adenocarcinoma and found that FAM83A-AS1 can serve as an independent risk prognostic factor in lung adenocarcinoma, potentially playing a promoting role in tumor development. Additionally, high expression of FAM83A-AS1 may be associated with higher drug sensitivity to gefitinib, afatinib, and savolitinib, providing a potential target for personalized treatment of lung adenocarcinoma. lung adenocarcinoma lncRNA bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cancer is one of the major global public health challenges and a leading cause of death in many countries. Smoking is widely recognized as a primary risk factor for lung cancer; however, the rising incidence of lung cancer among non-smokers in recent years suggests that other factors also play important roles in lung carcinogenesis [ 1 ] . As one of the most common malignant tumors, lung cancer is characterized by rapid progression, easy metastasis, high incidence, and high mortality [ 2 – 3 ] . Lung cancer is mainly divided into two major subtypes: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for the vast majority. Pathologically, NSCLC can be further classified into subtypes such as lung adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and adenosquamous carcinoma. Among these, lung adenocarcinoma is the most common histological type, accounting for approximately 50% of all NSCLC cases [ 4 ] . Lung cancer is highly heterogeneous, and its development involves various mechanisms such as gene mutations, chemical carcinogens, genetic factors, and hormones [ 5 ] . Patients with early-stage lung adenocarcinoma may have the opportunity for curative surgical resection, but the majority of patients are diagnosed at an advanced stage, requiring reliance on anticancer drugs for conservative treatment [ 6 ] . Several effective drugs, such as gemcitabine [ 7 ] , are currently used in the treatment of lung adenocarcinoma. At the molecular level, various genetic and molecular biomarkers (e.g., mRNA and non-coding RNAs) have been employed for the diagnosis and treatment of lung adenocarcinoma [ 8 ] . Non-coding RNAs (ncRNAs) are a class of RNA molecules that are largely transcribed from the genome but do not encode proteins. They include long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and others [ 9 ] . Among them, lncRNAs are defined as being longer than 200 nucleotides [ 10 ] . They can interact with mRNA, miRNA, circRNA, etc., participating in complex networks of gene expression regulation [ 11 ] . Due to their important roles in tumorigenesis, cell cycle, apoptosis, and chemotherapy resistance, lncRNAs have become a hotspot in current research [ 12 ] . In recent years, the application of the third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) Osimertinib has significantly improved the prognosis of patients with lung adenocarcinoma. However, acquired resistance still limits its efficacy. A deeper understanding of the biological processes and molecular mechanisms of lung cancer pathogenesis is of great significance for clinical diagnosis, treatment, and improving patient prognosis. In this context, developing reliable biomarkers for predicting the prognosis of lung adenocarcinoma is particularly urgent. This study aimed to screen differentially expressed lncRNAs in lung adenocarcinoma and further analyze key lncRNAs associated with prognosis. Ultimately, we identified three lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—that can serve as independent prognostic factors and play important roles in the occurrence and development of lung adenocarcinoma. Materials and Methods Data Source Gene expression data (535 tumor tissues and 59 normal lung tissues, data type: HTSeq-FPKM) and corresponding clinical information were downloaded from the official TCGA Lung Adenocarcinoma (LUAD) project website ( https://portal.gdc.cancer.gov/repository ). We used PERL ( https://www.perl.org/ ) and its accompanying scripts to organize the transcriptome data and convert gene IDs in the mRNA expression matrix. Analysis Tools In this study, R software version 4.0.3 ( https://www.r-project.org/ ) was used for data analysis and processing. The involved R packages included edgeR, pheatmap, limma, survival, survminer, timeROC, ggpubr, Reformae2, ConsensusClusterPlus, corrplot, caret, and glmnet. Differential Expression Gene Analysis Using PERL software, we constructed a gene expression matrix and a human configuration file. The R software, specifically the edgeR package, was used to identify differentially expressed lncRNAs between LUAD specimens and normal lung tissue specimens. The screening criteria were |logFC| >2 (FC, fold change) and FDR < 0.05. Differentially expressed genes with logFC 2 were defined as upregulated. The limma package was used to integrate the differentially expressed data with clinical survival data. Differentially expressed genes associated with prognosis were extracted, and the survival package was used to calculate confidence intervals and hazard ratios. The limma and pheatmap packages were employed to identify prognosis-related differentially expressed genes between tumor and normal tissue specimens. A p -value < 0.05 was considered statistically significant. Heatmaps were plotted to visualize the differences. Role of Prognosis-Related Differentially Expressed Genes Initially, based on the expression of prognosis-related differentially expressed genes, we used the ConsensusClusterPlus and limma packages to classify the prognosis-related differentially expressed genes into two subtypes, cluster1 and cluster2 ( https://bioconductor.org/install/ ), using clusterAlg='km' and clusterNum='2'. Survival analysis based on the subtypes was performed using the survminer package, and the prognostic value of the prognosis-related differentially expressed genes was evaluated using the survival package. A p -value < 0.05 was considered statistically significant. Construction of a Prognostic Model for Differentially Expressed Genes A predictive model was constructed using Lasso regression. All samples were divided into high-risk or low-risk groups based on the median risk score of the prognosis-related differentially expressed genes. In the Lasso regression, training (50%) and test (50%) groups were defined, and correlation plots were generated. Survival curves of the high-risk and low-risk groups were obtained and compared. To evaluate the accuracy of our model in predicting LUAD survival, corresponding ROC curves were generated using the timeROC package. Risk curves were plotted based on the risk scores, and the survival status and risk associated with the prognosis-related differentially expressed genes were evaluated using these curves. Finally, multivariate Cox analysis was used to assess the risk genes. Drug Sensitivity Analysis To investigate the relationship between FAM83A-AS1 and the drug sensitivity of common targeted therapies for lung cancer (gefitinib, afatinib, osimertinib, and savolitinib), the IC50 values of the four anti-tumor drugs were calculated, and differences between the high-expression and low-expression groups were compared. R packages including "pRRophetic", "ggpubr", "limma", and "ggplot2" were used [ 13 ] . Results Clinicopathological Characteristics of the LUAD Cohort from TCGA As shown in Table 1 , clinical and gene expression data from 522 primary tumors were downloaded from the TCGA database in July 2025. The median age at diagnosis was 67 years. In our study cohort, 279 cases (54.3%) were clinical stage I, 124 cases (24.1%) were stage II, 85 cases (16.5%) were stage III, and 26 cases (5.1%) were stage IV. Regarding tumor invasion, 172 cases (33.1%) were T1, 281 cases (54.1%) were T2, 47 cases (9.1%) were T3, and 19 cases (3.7%) were T4. Lymph node metastasis was present in 175 out of 510 cases (34.3%). Distant metastasis was found in 25 out of 378 cases (6.7%). The median follow-up time was 18.3 months (range: 0-227 months). Table 1 Clinicopathological characteristics of lung adenocarcinoma patients. Characteristic n(total = 522) (%) Median age 67 Gender Female 280(53.6%) Male 242(46.4%) Stage I 279(54.3%) II 124(24.1%) III 85(16.5%) IV 26(5.1%) T classification T1 172(33.1%) T2 281(54.1%) T3 47(9.1%) T4 19(3.7%) M classification M0 353(93.3%) M1 25(6.7%) N classification N0 335(65.7%) N1 98(19.2%) N2 75(14.7%) N3 2(0.4%) Differential Expression and Prognosis of LUAD Patients Using the edgeR package, we analyzed differentially expressed lncRNAs in LUAD tissues, identifying a total of 97 upregulated and 22 downregulated lncRNAs (Screening criteria: |log2FC| >2, FDR < 0.05). A volcano plot visually displayed the distribution of these differentially expressed genes (Fig. 1 A). Furthermore, univariate Cox regression analysis identified 17 lncRNAs significantly associated with overall survival (OS). Among these, the expression levels of AL590226.1, FENDRR, and C20orf197 were positively correlated with prognosis, meaning higher expression was associated with better survival (Fig. 1 B). Figure 1 C shows the expression patterns of these 17 prognosis-related lncRNAs, indicating that AC245041.2, AC245041.1, MYO16-AS1, FENDRR, and AL590226.1 were lowly expressed in tumor tissues, while the remaining genes were highly expressed. Additionally, Fig. 1 D shows the correlation coefficients among different lncRNAs, ranging from weak to strong. AL365181.3 and AL590666.2 showed the strongest positive correlation, meaning when AL365181.3 was highly expressed, AL590666.2 was also likely highly expressed. This was followed by FENDRR and AL590226.1, whose expressions were also positively correlated. The Role of Prognostic Differentially Expressed lncRNAs Based on the expression profiles of the prognosis-related differentially expressed lncRNAs, consensus clustering analysis determined that the overlap between clusters was minimal when K = 2 (Fig. 2 A), leading to the division of patients into two subtypes: cluster1 and cluster2. Survival analysis revealed that the prognosis of cluster1 was significantly better than that of cluster2 (p < 0.001, Fig. 2 C). A heatmap further displayed the expression patterns of the prognosis-related lncRNAs and their relationship with clinicopathological parameters (Fig. 2 D). Some lncRNAs were highly expressed in cluster1, while others were highly expressed in cluster2; however, no clear correlations were found between these genes and the clinicopathological parameters. Subsequently, a risk score model based on the prognosis-related differentially expressed genes was constructed using Lasso regression. All samples were divided into high-risk and low-risk groups based on the median risk score. The training and test sets were split in a 50% ratio, with corresponding diagrams shown in Figs. 3 A and B. Comparison of survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group in both the training and test sets (p < 0.05, Figs. 3 C, D). ROC curves generated using the timeROC package (Figs. 3 E, F) had areas under the curve (AUC) greater than 0.5, indicating good prognostic predictive accuracy of the model. The risk curve and survival status distribution are shown in Fig. 4 . As the risk score increased, the proportion of high-risk patients and the number of deaths gradually increased. Ultimately, eight lncRNAs significantly associated with risk were identified: AL590226.1, FAM83A-AS1, PPP1R14B-AS1, AC245041.1, AL365181.3, AC022784.1, UCA1, and C20orf197 (Figs. 4 A, B). Among these, six genes, including FAM83A-AS1, were highly expressed in the high-risk group, suggesting they might promote LUAD progression, whereas AL590226.1 and C20orf197 were highly expressed in the low-risk group, potentially having protective roles. Multivariate Cox regression analysis (Table 2 ) showed that three lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—could serve as independent prognostic factors (HR > 1, p < 0.05), indicating their significant value in LUAD prognosis assessment. Further progression-free survival (PFS) analysis revealed that only the expression of FAM83A-AS1 was significantly associated with PFS (Fig. 5 ). Therefore, the study focused on FAM83A-AS1 and constructed a nomogram incorporating clinical features to individually predict patient prognosis (Fig. 6 A). The calibration curve showed good consistency between the nomogram-predicted and actually observed survival rates at 1, 3, and 5 years (Fig. 6 B). Taking the example patient in Fig. 6 A: the patient is nearly 90 years old (score ~ 50 points), has a FAM83A-AS1 expression level between 1–2, and is at clinical stage I, resulting in a total score of 80.9 points. The predicted 1-year, 3-year, and 5-year survival rates for this patient are 90.8%, 68.2%, and 45.8%, respectively. Table 2 Multivariate Cox regression analysis. id HR HR.95L HR.95H p value AL365181.3 1.03348336 1.010321978 1.05717571 0.004400195 FAM83A-AS1 1.036355013 1.010066551 1.063327671 0.006449163 AC245041.1 1.079892169 1.018480001 1.14500736 0.010083938 PPP1R14B-AS1` 1.059523964 0.997587762 1.125305536 0.059920507 AL590226.1 0.678355636 0.389743218 1.180691151 0.169900598 UCA1 1.009305063 0.995290218 1.023517254 0.194203877 C20orf197 0.938564158 0.843161711 1.044761244 0.246328311 AC022784.1 1.009774607 0.992188891 1.027672015 0.277856756 Drug Sensitivity Analysis With the rapid development of molecular biology, lung cancer treatment has entered the era of targeted therapy. Gefitinib, afatinib, and osimertinib, as classic first-, second-, and third-generation EGFR-TKIs respectively, along with the emerging MET-TKI savolitinib, hold important positions in lung cancer treatment. Discovering genetic markers that can predict the efficacy of these drugs is of significant clinical value. Based on our previous finding—that FAM83A-AS1 serves as an independent prognostic factor in LUAD—we further analyzed the association between its expression and the sensitivity to the aforementioned four drugs. The results showed that in the high FAM83A-AS1 expression group, the IC50 values for gefitinib, afatinib, and savolitinib were significantly lower (Figs. 7 A, C, D), suggesting potentially better efficacy of these drugs for patients in this group. However, no statistically significant difference was observed for osimertinib (Fig. 7 B). Discussion Currently, developing personalized treatment strategies for malignant tumors remains a significant challenge due to tumor heterogeneity and complex genetic backgrounds. In the era of personalized medicine, accurate prognostic assessment for lung adenocarcinoma (LUAD) patients is crucial for clinical management and treatment. In recent years, with the deepening of genomic research and the advancement of biotechnology, the accumulation of vast biological data has greatly propelled progress in bioinformatics analytical methods [ 14 ] . Against this backdrop, research in natural product pharmacology has also gradually begun to utilize bioinformatics and network pharmacology to explore new research directions. Substantial evidence indicates that many long non-coding RNAs (lncRNAs) play key roles in the occurrence and development of LUAD. The genetic regulatory mechanisms of lncRNAs are complex, and their functions in cancer are not yet fully understood [ 15 ] . Compared to single clinical biomarkers, integrating multiple biomarkers into a unified model helps improve predictive accuracy and supports personalized treatment. Therefore, constructing a combinatorial model based on multiple lncRNAs holds promise for more accurately predicting patient prognosis [ 16 – 18 ] . Although some lncRNA markers associated with lung cancer have been reported, previous studies have often focused on progression-free survival (PFS) or overall survival (OS) in early-stage lung cancer patients [ 19 – 20 ] . A comprehensive prognostic model specifically for LUAD still requires further development. This study first identified 119 differentially expressed lncRNAs (DE-lncRNAs) using the edgeR package, comprising 97 upregulated and 22 downregulated lncRNAs. The larger number of upregulated lncRNAs suggests they might play more significant roles in the biological behavior of LUAD. Further multivariate Cox regression analysis screened for lncRNAs associated with prognosis, and patients were divided into two subtypes based on these, with cluster 1 showing significantly better survival prognosis than cluster 2. Based on this, we constructed a predictive model incorporating 8 prognostic risk lncRNAs. Among these 8 lncRNAs, AL590226.1 and C20orf197 might be protective factors associated with favorable prognosis, whereas FAM83A-AS1, PPP1R14B-AS1, AC245041.1, AL365181.3, AC022784.1, and UCA1 were identified as high-risk factors. Zhou et al. [ 21 ] reported that PPP1R14B-AS1 is highly expressed in breast cancer and associated with poor prognosis, promoting malignant progression by regulating the miR-134-3p/LASP1 axis. Similarly, our results found PPP1R14B-AS1 highly expressed in tumor tissues and identified it as a risk gene. Liu et al. [ 22 ] found that knocking down AL365181.3 suppressed the proliferation, migration, and invasion of LUAD cells and reduced tumorigenicity in vivo, further supporting its cancer-promoting role. Research on the other lncRNAs remains limited. To evaluate the model's effectiveness, we plotted ROC curves and calculated the area under the curve (AUC). All AUC values exceeded 0.60, indicating good predictive performance of the model. Finally, multivariate Cox analysis confirmed that three lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—could serve as novel independent prognostic biomarkers for LUAD. In summary, this study not only reveals the potential roles of specific lncRNAs in LUAD but also proposes a new combination of markers. Previously, Guo et al. [ 23 ] suggested through bioinformatics analysis that FAM83A-AS1 is associated with LUAD progression; multiple studies have also indicated that this gene promotes tumor cell migration and invasion [ 24 – 25 ] . Jia et al. [ 26 ] found that FAM83A-AS1 promotes tumor progression in esophageal squamous cell carcinoma by regulating the miR-214/CDC25B axis, consistent with its identification as a high-risk factor in our study. Furthermore, we are the first to report that AC245041.1 and AL365181.3 might serve as poor prognostic factors in LUAD, although their specific mechanisms in tumorigenesis warrant further exploration. This study successfully established a lncRNA-based prognostic model and identified the value of three risk lncRNAs as independent prognostic factors. Additionally, sensitivity analysis for commonly used targeted drugs (gefitinib, afatinib, osimertinib, and savolitinib) showed that the high FAM83A-AS1 expression group might be more sensitive to gefitinib, afatinib, and savolitinib. This finding could provide a new direction for clinical treatment strategies and possesses high translational potential. We also explored the relationship between different molecular subtypes and the prognostic model. However, we recognize several limitations in this study: Firstly, the bioinformatics analysis methods have limited innovation, and the samples were sourced only from LUAD rather than all lung cancer types. Secondly, the data relied entirely on the TCGA database. Although internal validation was performed using various methods, external validation with independent cohorts is still needed, and the current conclusions might be limited in their clinical applicability. In the future, we plan to conduct biological experiments and larger-scale clinical studies to deeply explore the downstream signaling mechanisms of these lncRNAs and validate their functions in LUAD through experimental approaches. In conclusion, this study innovatively constructed a well-performing prognostic prediction model for LUAD, which can be used to guide clinical treatment decisions. Future large-sample studies are necessary to verify its reliability and promote its translation into clinical application. Abbreviations RNA: Ribonucleic Acid, FAM83A-AS1: FAM83A antisense RNA 1, miRNA: microRNA, circRNA: CircularRNA, Declarations Acknowledgements ： I would like to express my gratitude to all those who helped me during the writing of this thesis. I gratefully acknowledge the help of my supervisor, Mr. Man who has offered me valuable suggestions in the academic studies. In the preparation of the thesis, he has spent much time reading through each draft and provided me with inspiring advice. Without his patient instruction, insightful criticism and expert guidance, the completion of this thesis would not have been possible. Funding: 2025 Jiangsu Provincial Young Scientific and Technological Talent Support Project (STJ-2025-954). Yangzhou Basic Research Program (Joint Special Project) - Health and Wellness Category Funded Projects (General Program: 2025-2-25) Zhenjiang Science and Technology Plan Projects: Municipal Key R&D Program (Social Development) Authors' contributions: Changfen Man provided direction and guidance throughout the preparation of this manuscript. Binbn Hu wrote and edited the manuscript. Xiangwei Xia reviewed and made significant revisions to the manuscript. All authors read and approved the final manuscript. Ethical Approval and Consent to participate: Not applicable. Consent to publication: All the authors approved the publication of this manuscript. Clinical trial: not applicable Conflict of interest statement: The authors declare that they have no competing interests. References Raaschou-Nielsen Ole, Andersen Zorana J, Beelen Rob, et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE) [J]. Lancet Oncol. 2013, 14(9): 813–822. DOI: 10.1016/S1470-2045(13)70444-3. Smith RA, Andrews KS, Brooks D, er al. Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening [J]. CA Cancer J Clin. 2019, 69(3):184-210. DOI: 10.3322/caac.21557. Tang H, Han X, Feng Y, et al. linc00968 inhibits the tumorigenesis and metastasis of lung adenocarcinoma via serving as a ceRNA against miR-9-5p and increasing CPEB3 [J]. 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LncRNA FAM83A-AS1 promotes ESCC progression by regulating miR-214/CDC25B axis [J]. J Cancer. 2021, 12(4):1200-1211. DOI: 10.7150/jca.54007. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7767452","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523859623,"identity":"106a2129-e4ef-4eff-9570-d35f4b18a020","order_by":0,"name":"Binbin Hu","email":"","orcid":"","institution":"Gaoyou People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Hu","suffix":""},{"id":523859624,"identity":"3405550c-cc61-4917-80a2-aaff37a8aad8","order_by":1,"name":"Xiangwei Xia","email":"","orcid":"","institution":"Gaoyou People’s 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11:42:48","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84199,"visible":true,"origin":"","legend":"","description":"","filename":"53cd29e948f1423a884b88d833c84bf71structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/fd6891e28b8d8266ef3c087b.xml"},{"id":92802760,"identity":"253ba4c2-d9e3-441b-a484-9e715b35f169","added_by":"auto","created_at":"2025-10-05 11:42:47","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90787,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/75a3dbb60baed288858c2118.html"},{"id":92802748,"identity":"a47a3fa4-c6cb-43b2-b631-9ae958d98e6d","added_by":"auto","created_at":"2025-10-05 11:42:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346725,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression and their prognostic role in lung adenocarcinoma patients.\u003cbr\u003e\n(A) Volcano plot of differentially expressed genes in lung adenocarcinoma (fold change \u0026gt;2 or \u0026lt;-2).\u003cbr\u003e\n(B) Forest plot of univariate Cox regression analysis for differentially expressed genes. Red represents high risk, while green represents low risk.\u003cbr\u003e\n(C) Heatmap of the expression of prognosis-related differentially expressed genes. Red indicates high expression, while blue indicates low expression. The x-axis represents samples, and the y-axis represents prognosis-related differentially expressed genes.\u003cbr\u003e\n(D) Spearman correlation analysis of differentially expressed prognostic lncRNAs (red indicates positive correlation, blue indicates negative correlation). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/7fadb4782a7c54267396a121.png"},{"id":92802753,"identity":"d1e71dc3-c67c-4c45-b312-2cb39ab2fadf","added_by":"auto","created_at":"2025-10-05 11:42:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372873,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and relationships of prognosis-associated differentially expressed lncRNAs.\u003cbr\u003e\n(A, B) Consensus clustering matrix at k=2.\u003cbr\u003e\n(C) Survival analysis based on differential lncRNA subtypes.\u003cbr\u003e\n(D) Association between prognosis-related differentially expressed genes and clinicopathological parameters\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/8c2133dabdc629ef82f57556.png"},{"id":92802750,"identity":"74a01a43-911a-4876-9cca-23c6e265c220","added_by":"auto","created_at":"2025-10-05 11:42:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202424,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic model for lung adenocarcinoma patients and its impact on survival outcomes.\u003cbr\u003e\n(A+B) Establishment of the predictive model using LASSO regression.\u003cbr\u003e\n(C+D) Survival curves for each group. In both groups, patients in the low-risk category showed significantly higher survival rates compared to those in the high-risk category (C: test cohort, D: training cohort, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003cbr\u003e\n(E+F) ROC curves evaluating the accuracy of our model in predicting patient survival (E: test cohort, F: training cohort, area under the curve \u0026gt; 0.5). The model effectively predicts prognosis in lung adenocarcinoma patients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/cf179d4b0aac0ceb9b311556.png"},{"id":92803063,"identity":"440fff31-0b45-4667-bb64-0aa7c9d1072e","added_by":"auto","created_at":"2025-10-05 11:50:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":277126,"visible":true,"origin":"","legend":"\u003cp\u003eRisk scores associated with prognosis-related differentially expressed lncRNAs and their impact on the prognosis of lung adenocarcinoma patients.\u003cbr\u003e\n(A+B) Risk-associated heatmap: LncRNAs including FAM83A-AS1, PPP1R14B-AS1, AC245041.1, AL365181.3, AC022784.1, and UCA1 showed high expression in the high-risk group, suggesting their potential adverse effects on patient prognosis. Conversely, AL590226.1 and C20orf197 were highly expressed in the low-risk group, indicating their potential protective roles in lung adenocarcinoma prognosis.\u003cbr\u003e\n(C+D) Risk score distribution curves and (E+F) Risk-associated scatter plots. As the risk score increases, there is a corresponding rise in mortality rates and the proportion of high-risk patients. A+C+E: test cohort; B+D+F: training cohort.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/d9e9bcb3cfc86bc6817e0ed9.png"},{"id":92802752,"identity":"86d6ff40-d5e7-4226-ab80-0b1b9480b9f5","added_by":"auto","created_at":"2025-10-05 11:42:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":139576,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram and calibration plot.\u003cbr\u003e\n(A) Nomogram constructed based on risk score and clinical characteristics.\u003cbr\u003e\n(B) Calibration plot of the nomogram for predicting 1-year, 3-year, and 5-year survival rates.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/f11f92d9c0bb5d34d4bf09b2.png"},{"id":92802764,"identity":"ccb77431-063c-4bc8-9db8-4ebfda24d41d","added_by":"auto","created_at":"2025-10-05 11:42:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93774,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis of FAM83A-AS1.\u003c/p\u003e\n\u003cp\u003e(A) Gefitinib (B) Osimertinib (C) Savolitinib (D) Afatinib\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/afb9db25cdb82bfae1d2472a.png"},{"id":99686924,"identity":"578a8fd8-16ba-4998-ba20-d640b5a2d0db","added_by":"auto","created_at":"2026-01-07 09:40:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2089217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7767452/v1/843a5d0d-c4e3-41df-87f0-ecd66dabd72f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is one of the major global public health challenges and a leading cause of death in many countries. Smoking is widely recognized as a primary risk factor for lung cancer; however, the rising incidence of lung cancer among non-smokers in recent years suggests that other factors also play important roles in lung carcinogenesis \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. As one of the most common malignant tumors, lung cancer is characterized by rapid progression, easy metastasis, high incidence, and high mortality \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Lung cancer is mainly divided into two major subtypes: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for the vast majority. Pathologically, NSCLC can be further classified into subtypes such as lung adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and adenosquamous carcinoma. Among these, lung adenocarcinoma is the most common histological type, accounting for approximately 50% of all NSCLC cases \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Lung cancer is highly heterogeneous, and its development involves various mechanisms such as gene mutations, chemical carcinogens, genetic factors, and hormones \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Patients with early-stage lung adenocarcinoma may have the opportunity for curative surgical resection, but the majority of patients are diagnosed at an advanced stage, requiring reliance on anticancer drugs for conservative treatment \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Several effective drugs, such as gemcitabine \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, are currently used in the treatment of lung adenocarcinoma. At the molecular level, various genetic and molecular biomarkers (e.g., mRNA and non-coding RNAs) have been employed for the diagnosis and treatment of lung adenocarcinoma \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNon-coding RNAs (ncRNAs) are a class of RNA molecules that are largely transcribed from the genome but do not encode proteins. They include long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and others \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Among them, lncRNAs are defined as being longer than 200 nucleotides \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. They can interact with mRNA, miRNA, circRNA, etc., participating in complex networks of gene expression regulation \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Due to their important roles in tumorigenesis, cell cycle, apoptosis, and chemotherapy resistance, lncRNAs have become a hotspot in current research \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent years, the application of the third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) Osimertinib has significantly improved the prognosis of patients with lung adenocarcinoma. However, acquired resistance still limits its efficacy. A deeper understanding of the biological processes and molecular mechanisms of lung cancer pathogenesis is of great significance for clinical diagnosis, treatment, and improving patient prognosis. In this context, developing reliable biomarkers for predicting the prognosis of lung adenocarcinoma is particularly urgent. This study aimed to screen differentially expressed lncRNAs in lung adenocarcinoma and further analyze key lncRNAs associated with prognosis. Ultimately, we identified three lncRNAs\u0026mdash;AL365181.3, FAM83A-AS1, and AC245041.1\u0026mdash;that can serve as independent prognostic factors and play important roles in the occurrence and development of lung adenocarcinoma.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Source\u003c/h2\u003e\u003cp\u003eGene expression data (535 tumor tissues and 59 normal lung tissues, data type: HTSeq-FPKM) and corresponding clinical information were downloaded from the official TCGA Lung Adenocarcinoma (LUAD) project website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/repository\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We used PERL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.perl.org/\u003c/span\u003e\u003cspan address=\"https://www.perl.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and its accompanying scripts to organize the transcriptome data and convert gene IDs in the mRNA expression matrix.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis Tools\u003c/h3\u003e\n\u003cp\u003eIn this study, R software version 4.0.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for data analysis and processing. The involved R packages included edgeR, pheatmap, limma, survival, survminer, timeROC, ggpubr, Reformae2, ConsensusClusterPlus, corrplot, caret, and glmnet.\u003c/p\u003e\n\u003ch3\u003eDifferential Expression Gene Analysis\u003c/h3\u003e\n\u003cp\u003eUsing PERL software, we constructed a gene expression matrix and a human configuration file. The R software, specifically the edgeR package, was used to identify differentially expressed lncRNAs between LUAD specimens and normal lung tissue specimens. The screening criteria were |logFC| \u0026gt;2 (FC, fold change) and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differentially expressed genes with logFC \u0026lt; -2 were defined as downregulated, while those with logFC\u0026thinsp;\u0026gt;\u0026thinsp;2 were defined as upregulated. The limma package was used to integrate the differentially expressed data with clinical survival data. Differentially expressed genes associated with prognosis were extracted, and the survival package was used to calculate confidence intervals and hazard ratios. The limma and pheatmap packages were employed to identify prognosis-related differentially expressed genes between tumor and normal tissue specimens. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Heatmaps were plotted to visualize the differences.\u003c/p\u003e\n\u003ch3\u003eRole of Prognosis-Related Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eInitially, based on the expression of prognosis-related differentially expressed genes, we used the ConsensusClusterPlus and limma packages to classify the prognosis-related differentially expressed genes into two subtypes, cluster1 and cluster2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/install/\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/install/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using clusterAlg='km' and clusterNum='2'. Survival analysis based on the subtypes was performed using the survminer package, and the prognostic value of the prognosis-related differentially expressed genes was evaluated using the survival package. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eConstruction of a Prognostic Model for Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eA predictive model was constructed using Lasso regression. All samples were divided into high-risk or low-risk groups based on the median risk score of the prognosis-related differentially expressed genes. In the Lasso regression, training (50%) and test (50%) groups were defined, and correlation plots were generated. Survival curves of the high-risk and low-risk groups were obtained and compared. To evaluate the accuracy of our model in predicting LUAD survival, corresponding ROC curves were generated using the timeROC package. Risk curves were plotted based on the risk scores, and the survival status and risk associated with the prognosis-related differentially expressed genes were evaluated using these curves. Finally, multivariate Cox analysis was used to assess the risk genes.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e\u003cp\u003eTo investigate the relationship between FAM83A-AS1 and the drug sensitivity of common targeted therapies for lung cancer (gefitinib, afatinib, osimertinib, and savolitinib), the IC50 values of the four anti-tumor drugs were calculated, and differences between the high-expression and low-expression groups were compared. R packages including \"pRRophetic\", \"ggpubr\", \"limma\", and \"ggplot2\" were used \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eClinicopathological Characteristics of the LUAD Cohort from TCGA\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, clinical and gene expression data from 522 primary tumors were downloaded from the TCGA database in July 2025. The median age at diagnosis was 67 years. In our study cohort, 279 cases (54.3%) were clinical stage I, 124 cases (24.1%) were stage II, 85 cases (16.5%) were stage III, and 26 cases (5.1%) were stage IV. Regarding tumor invasion, 172 cases (33.1%) were T1, 281 cases (54.1%) were T2, 47 cases (9.1%) were T3, and 19 cases (3.7%) were T4. Lymph node metastasis was present in 175 out of 510 cases (34.3%). Distant metastasis was found in 25 out of 378 cases (6.7%). The median follow-up time was 18.3 months (range: 0-227 months).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological characteristics of lung adenocarcinoma patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en(total\u0026thinsp;=\u0026thinsp;522) (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280(53.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242(46.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e279(54.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124(24.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85(16.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26(5.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172(33.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e281(54.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47(9.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19(3.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e353(93.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(6.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e335(65.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98(19.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75(14.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2(0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDifferential Expression and Prognosis of LUAD Patients\u003c/h2\u003e\u003cp\u003eUsing the edgeR package, we analyzed differentially expressed lncRNAs in LUAD tissues, identifying a total of 97 upregulated and 22 downregulated lncRNAs (Screening criteria: |log2FC| \u0026gt;2, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A volcano plot visually displayed the distribution of these differentially expressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Furthermore, univariate Cox regression analysis identified 17 lncRNAs significantly associated with overall survival (OS). Among these, the expression levels of AL590226.1, FENDRR, and C20orf197 were positively correlated with prognosis, meaning higher expression was associated with better survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC shows the expression patterns of these 17 prognosis-related lncRNAs, indicating that AC245041.2, AC245041.1, MYO16-AS1, FENDRR, and AL590226.1 were lowly expressed in tumor tissues, while the remaining genes were highly expressed. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD shows the correlation coefficients among different lncRNAs, ranging from weak to strong. AL365181.3 and AL590666.2 showed the strongest positive correlation, meaning when AL365181.3 was highly expressed, AL590666.2 was also likely highly expressed. This was followed by FENDRR and AL590226.1, whose expressions were also positively correlated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eThe Role of Prognostic Differentially Expressed lncRNAs\u003c/h2\u003e\u003cp\u003eBased on the expression profiles of the prognosis-related differentially expressed lncRNAs, consensus clustering analysis determined that the overlap between clusters was minimal when K\u0026thinsp;=\u0026thinsp;2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), leading to the division of patients into two subtypes: cluster1 and cluster2. Survival analysis revealed that the prognosis of cluster1 was significantly better than that of cluster2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). A heatmap further displayed the expression patterns of the prognosis-related lncRNAs and their relationship with clinicopathological parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Some lncRNAs were highly expressed in cluster1, while others were highly expressed in cluster2; however, no clear correlations were found between these genes and the clinicopathological parameters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, a risk score model based on the prognosis-related differentially expressed genes was constructed using Lasso regression. All samples were divided into high-risk and low-risk groups based on the median risk score. The training and test sets were split in a 50% ratio, with corresponding diagrams shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B. Comparison of survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group in both the training and test sets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). ROC curves generated using the timeROC package (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F) had areas under the curve (AUC) greater than 0.5, indicating good prognostic predictive accuracy of the model. The risk curve and survival status distribution are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. As the risk score increased, the proportion of high-risk patients and the number of deaths gradually increased. Ultimately, eight lncRNAs significantly associated with risk were identified: AL590226.1, FAM83A-AS1, PPP1R14B-AS1, AC245041.1, AL365181.3, AC022784.1, UCA1, and C20orf197 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Among these, six genes, including FAM83A-AS1, were highly expressed in the high-risk group, suggesting they might promote LUAD progression, whereas AL590226.1 and C20orf197 were highly expressed in the low-risk group, potentially having protective roles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMultivariate Cox regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed that three lncRNAs\u0026mdash;AL365181.3, FAM83A-AS1, and AC245041.1\u0026mdash;could serve as independent prognostic factors (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating their significant value in LUAD prognosis assessment. Further progression-free survival (PFS) analysis revealed that only the expression of FAM83A-AS1 was significantly associated with PFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, the study focused on FAM83A-AS1 and constructed a nomogram incorporating clinical features to individually predict patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The calibration curve showed good consistency between the nomogram-predicted and actually observed survival rates at 1, 3, and 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Taking the example patient in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA: the patient is nearly 90 years old (score\u0026thinsp;~\u0026thinsp;50 points), has a FAM83A-AS1 expression level between 1\u0026ndash;2, and is at clinical stage I, resulting in a total score of 80.9 points. The predicted 1-year, 3-year, and 5-year survival rates for this patient are 90.8%, 68.2%, and 45.8%, respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox regression analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR.95L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR.95H\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL365181.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03348336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.010321978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05717571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004400195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAM83A-AS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.036355013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.010066551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.063327671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.006449163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC245041.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.079892169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.018480001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.14500736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010083938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP1R14B-AS1`\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.059523964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.997587762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.125305536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059920507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAL590226.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.678355636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.389743218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.180691151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.169900598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCA1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.009305063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.995290218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.023517254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.194203877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC20orf197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.938564158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.843161711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.044761244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.246328311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAC022784.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.009774607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.992188891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.027672015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.277856756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e\u003cp\u003eWith the rapid development of molecular biology, lung cancer treatment has entered the era of targeted therapy. Gefitinib, afatinib, and osimertinib, as classic first-, second-, and third-generation EGFR-TKIs respectively, along with the emerging MET-TKI savolitinib, hold important positions in lung cancer treatment. Discovering genetic markers that can predict the efficacy of these drugs is of significant clinical value. Based on our previous finding\u0026mdash;that FAM83A-AS1 serves as an independent prognostic factor in LUAD\u0026mdash;we further analyzed the association between its expression and the sensitivity to the aforementioned four drugs. The results showed that in the high FAM83A-AS1 expression group, the IC50 values for gefitinib, afatinib, and savolitinib were significantly lower (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, C, D), suggesting potentially better efficacy of these drugs for patients in this group. However, no statistically significant difference was observed for osimertinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, developing personalized treatment strategies for malignant tumors remains a significant challenge due to tumor heterogeneity and complex genetic backgrounds. In the era of personalized medicine, accurate prognostic assessment for lung adenocarcinoma (LUAD) patients is crucial for clinical management and treatment. In recent years, with the deepening of genomic research and the advancement of biotechnology, the accumulation of vast biological data has greatly propelled progress in bioinformatics analytical methods \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Against this backdrop, research in natural product pharmacology has also gradually begun to utilize bioinformatics and network pharmacology to explore new research directions.\u003c/p\u003e\u003cp\u003eSubstantial evidence indicates that many long non-coding RNAs (lncRNAs) play key roles in the occurrence and development of LUAD. The genetic regulatory mechanisms of lncRNAs are complex, and their functions in cancer are not yet fully understood \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Compared to single clinical biomarkers, integrating multiple biomarkers into a unified model helps improve predictive accuracy and supports personalized treatment. Therefore, constructing a combinatorial model based on multiple lncRNAs holds promise for more accurately predicting patient prognosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Although some lncRNA markers associated with lung cancer have been reported, previous studies have often focused on progression-free survival (PFS) or overall survival (OS) in early-stage lung cancer patients \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. A comprehensive prognostic model specifically for LUAD still requires further development.\u003c/p\u003e\u003cp\u003eThis study first identified 119 differentially expressed lncRNAs (DE-lncRNAs) using the edgeR package, comprising 97 upregulated and 22 downregulated lncRNAs. The larger number of upregulated lncRNAs suggests they might play more significant roles in the biological behavior of LUAD. Further multivariate Cox regression analysis screened for lncRNAs associated with prognosis, and patients were divided into two subtypes based on these, with cluster 1 showing significantly better survival prognosis than cluster 2. Based on this, we constructed a predictive model incorporating 8 prognostic risk lncRNAs. Among these 8 lncRNAs, AL590226.1 and C20orf197 might be protective factors associated with favorable prognosis, whereas FAM83A-AS1, PPP1R14B-AS1, AC245041.1, AL365181.3, AC022784.1, and UCA1 were identified as high-risk factors. Zhou et al. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e reported that PPP1R14B-AS1 is highly expressed in breast cancer and associated with poor prognosis, promoting malignant progression by regulating the miR-134-3p/LASP1 axis. Similarly, our results found PPP1R14B-AS1 highly expressed in tumor tissues and identified it as a risk gene. Liu et al. \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e found that knocking down AL365181.3 suppressed the proliferation, migration, and invasion of LUAD cells and reduced tumorigenicity in vivo, further supporting its cancer-promoting role. Research on the other lncRNAs remains limited.\u003c/p\u003e\u003cp\u003eTo evaluate the model's effectiveness, we plotted ROC curves and calculated the area under the curve (AUC). All AUC values exceeded 0.60, indicating good predictive performance of the model. Finally, multivariate Cox analysis confirmed that three lncRNAs\u0026mdash;AL365181.3, FAM83A-AS1, and AC245041.1\u0026mdash;could serve as novel independent prognostic biomarkers for LUAD. In summary, this study not only reveals the potential roles of specific lncRNAs in LUAD but also proposes a new combination of markers.\u003c/p\u003e\u003cp\u003ePreviously, Guo et al. \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e suggested through bioinformatics analysis that FAM83A-AS1 is associated with LUAD progression; multiple studies have also indicated that this gene promotes tumor cell migration and invasion \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Jia et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e found that FAM83A-AS1 promotes tumor progression in esophageal squamous cell carcinoma by regulating the miR-214/CDC25B axis, consistent with its identification as a high-risk factor in our study. Furthermore, we are the first to report that AC245041.1 and AL365181.3 might serve as poor prognostic factors in LUAD, although their specific mechanisms in tumorigenesis warrant further exploration.\u003c/p\u003e\u003cp\u003eThis study successfully established a lncRNA-based prognostic model and identified the value of three risk lncRNAs as independent prognostic factors. Additionally, sensitivity analysis for commonly used targeted drugs (gefitinib, afatinib, osimertinib, and savolitinib) showed that the high FAM83A-AS1 expression group might be more sensitive to gefitinib, afatinib, and savolitinib. This finding could provide a new direction for clinical treatment strategies and possesses high translational potential. We also explored the relationship between different molecular subtypes and the prognostic model. However, we recognize several limitations in this study: Firstly, the bioinformatics analysis methods have limited innovation, and the samples were sourced only from LUAD rather than all lung cancer types. Secondly, the data relied entirely on the TCGA database. Although internal validation was performed using various methods, external validation with independent cohorts is still needed, and the current conclusions might be limited in their clinical applicability. In the future, we plan to conduct biological experiments and larger-scale clinical studies to deeply explore the downstream signaling mechanisms of these lncRNAs and validate their functions in LUAD through experimental approaches.\u003c/p\u003e\u003cp\u003eIn conclusion, this study innovatively constructed a well-performing prognostic prediction model for LUAD, which can be used to guide clinical treatment decisions. Future large-sample studies are necessary to verify its reliability and promote its translation into clinical application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRNA: Ribonucleic Acid, FAM83A-AS1: FAM83A antisense RNA 1, miRNA: microRNA, circRNA: CircularRNA,\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to express my gratitude to all those who helped me during the writing of this thesis. I gratefully acknowledge the help of my supervisor, Mr. Man who has offered me valuable suggestions in the academic studies. In the preparation of the thesis, he has spent much time reading through each draft and provided me with inspiring advice. Without his patient instruction, insightful criticism and expert guidance, the completion of this thesis would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2025 Jiangsu Provincial Young Scientific and Technological Talent Support Project (STJ-2025-954).\u003c/p\u003e\n\u003cp\u003eYangzhou Basic Research Program (Joint Special Project) - Health and Wellness Category Funded Projects (General Program: 2025-2-25)\u003c/p\u003e\n\u003cp\u003eZhenjiang Science and Technology Plan Projects: Municipal Key R\u0026amp;D Program (Social Development)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eChangfen Man provided direction and guidance throughout the preparation of this manuscript. Binbn Hu wrote and edited the manuscript. Xiangwei Xia reviewed and made significant revisions to the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors approved the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRaaschou-Nielsen Ole, Andersen Zorana J, Beelen Rob, et al. 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DOI: 10.7150/jca.54007. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung adenocarcinoma, lncRNA, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-7767452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7767452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aims to investigate the role of lncRNAs in lung adenocarcinoma using bioinformatics methods.\u003cbr\u003e\n \u003cstrong\u003eMethods: \u003c/strong\u003eGene expression profiles and clinical data of lung adenocarcinoma patients were extracted from the TCGA database. PERL software was used to distinguish between mRNA and lncRNA, and edgeR software was employed to identify differentially expressed lncRNAs. Univariate Cox regression was then applied to further screen for differentially expressed lncRNAs associated with prognosis. A predictive model was constructed using Lasso regression, followed by the generation of risk scores to distinguish between high-risk and low-risk groups. ROC curves were generated to visualize the predictive ability of the current features for the disease. Multivariate Cox regression analysis was used to evaluate risk genes.\u003cbr\u003e\n \u003cstrong\u003eResults: \u003c/strong\u003eWe identified 119 differentially expressed lncRNAs, of which 97 were upregulated and 22 were downregulated. Seventeen differentially expressed lncRNAs were associated with survival prognosis. We further investigated the role of these prognostic differentially expressed lncRNAs in lung adenocarcinoma. The prognostic differentially expressed lncRNAs were divided into two subgroups, and it was found that the survival of cluster 1 was better than that of cluster 2. A prognostic model was then established, and the results indicated eight risk lncRNAs. Finally, multivariate Cox regression analysis revealed that three risk lncRNAs—AL365181.3, FAM83A-AS1, and AC245041.1—could serve as independent prognostic factors and play important roles in lung adenocarcinoma.\u003cbr\u003e\n \u003cstrong\u003eConclusion: \u003c/strong\u003eUsing bioinformatics methods, we preliminarily explored the role of lncRNAs in lung adenocarcinoma and found that FAM83A-AS1 can serve as an independent risk prognostic factor in lung adenocarcinoma, potentially playing a promoting role in tumor development. Additionally, high expression of FAM83A-AS1 may be associated with higher drug sensitivity to gefitinib, afatinib, and savolitinib, providing a potential target for personalized treatment of lung adenocarcinoma.\u003c/p\u003e","manuscriptTitle":"Study on the mechanism of lncRNAs in lung adenocarcinoma based on Bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-05 11:42:42","doi":"10.21203/rs.3.rs-7767452/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":"6f64fbfa-043e-4143-8398-8e5257e7654d","owner":[],"postedDate":"October 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T09:39:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-05 11:42:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7767452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7767452","identity":"rs-7767452","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}