Immune-Related lncRNA Signatures Define Tumor Microenvironment Subtypes and Predict Immunotherapy Response in NSCLC

Immune-Related lncRNA Signatures Define Tumor Microenvironment Subtypes and Predict Immunotherapy Response in NSCLC | 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 Immune-Related lncRNA Signatures Define Tumor Microenvironment Subtypes and Predict Immunotherapy Response in NSCLC Ang Li, Yutao Pang, Xiao Yang, Hongfei Zhang, Dong Wu, Liyao Lin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7575439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Long non-coding RNAs (lncRNAs) play critical roles in immune regulation and tumor microenvironment (TME) remodeling. However, their contribution to non-small cell lung cancer (NSCLC) heterogeneity and immunotherapy response remains unclear. Methods We integrated transcriptomic data from TCGA and GEO cohorts after batch correction. Immune-related lncRNAs were identified and used for unsupervised clustering to define molecular subtypes. Survival outcomes, immune infiltration, somatic mutation profiles, and predicted drug sensitivities were compared among subtypes. Weighted gene co-expression network analysis (WGCNA) and pathway enrichment were performed to identify hub genes and biological processes. Results Three lncRNA-defined subtypes were identified with distinct TME characteristics: an immune-inflamed subtype enriched in B/T cells and HLA expression, an immune-escape subtype with interferon-driven MHC upregulation, and an immune-desert subtype with minimal immune infiltration. These subtypes were significantly associated with prognosis, genomic alterations, and clinical features. Cluster A (predominantly LUAD) exhibited superior overall survival and higher predicted immunotherapy sensitivity, while Cluster C (enriched in LUSC) showed greater responsiveness to chemotherapy. Hub genes including SOX2, KRAS, KEAP1, and STAT1 were implicated in TME regulation. Drug sensitivity prediction suggested potential therapeutic stratification across clusters. Conclusions Immune-related lncRNA signatures define novel NSCLC subtypes with distinct immune phenotypes and therapeutic responses. These findings suggest that lncRNA-based TME classification may complement PD-L1 and tumor mutational burden as predictive biomarkers for immunotherapy. Validation in prospective clinical cohorts is warranted to establish their translational utility. Non-small cell lung cancer immune related lncRNA Tumor immune microenvironment Immune checkpoint inhibitors Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lung cancer holds the highest incidence and mortality rates worldwide, with non-small cell lung cancer (NSCLC) constituting approximately 85% of cases[ 1 ]. Despite advancements in early diagnosis and treatment, which include surgery, radiochemotherapy, and targeted therapy, the 5-year overall survival rate for patients with NSCLC remains less than 30%[ 2 ]. Therefore, continued research focused on molecular biomarkers and novel therapies is crucial for prognosis prediction and individualized treatment in NSCLC. Over the last decade, the introduction of immune checkpoint inhibitors (ICI) such as programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors has transformed the management of NSCLC[ 3 ]. However, immunotherapy is not suitable for all patients, with only about 30% of lung cancer patients experiencing benefits from this form of treatment[ 4 ]. Various biomarkers, including PD-L1 expression, tumor mutation burden (TMB), and microsatellite instability (MSI), have been reported to predict the response of solid tumors to immunotherapy, albeit with average prediction ability[ 5 ]. With a length longer than 200 nucleotides, long non-coding RNAs (lncRNAs) do not encode proteins but participate in various cellular processes, including cell proliferation, differentiation, apoptosis, and metastasis[ 6 ]. Additionally, growing research has demonstrated that lncRNAs emerge as critical regulators of the immune system by directing the expression of immune-related genes at epigenetic, transcriptional, and post-transcriptional levels[ 7 – 9 ]. For instance, the lncRNA NKILA has been found to regulate T cell sensitivity to activation-induced cell death in lung cancer microenvironments[ 10 ]. The lncRNA lincRNA-Cox2 has been demonstrated to mediate the activation and suppression of diverse immune genes to control the inflammatory response[ 11 ]. Another lncRNA, LINK-A, has been proven to impair cancer cell antigen presentation and intrinsic tumor suppression[ 12 ]. Collectively, these studies indicate that lncRNAs serve as vital regulators in tumor immunology. More recently, increasing evidence indicates that the tumor microenvironment (TME), consisting of a mixture of tumor cells with tumor-infiltrating immune cells and stromal components, plays a critical role in tumor proliferation, invasion, and metastasis[ 13 ]. Interactions between immune cells and cytokines in the TME modulate the response to immunotherapy. In this study, we hypothesized that lncRNAs could shape the tumor microenvironment and modulate the response to immunotherapy. We subsequently established three clinically relevant NSCLC subtypes based on immune-related lncRNAs. Through analysis of immune infiltration, pathway analysis, and driver genes, we aimed to explore the similarities and differences between each subtype. Additionally, we analyzed the sensitivity of different subtypes to immunity, targeted therapy, and chemotherapy. These findings are expected to contribute to the diagnosis and treatment of non-small cell lung cancer. Materials and Methods Dataset Source Retrieval and Preprocessing NSCLC sample datasets with transcriptional profiles data (GSE30219, GSE50081, GPL570) were obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo , accessed June 2020) and The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov , accessed June, 2020). NSCLC Patients with clinical stage I-III were included in this study. Exclusion criteria include mixed histology, inadequate or poor-quality samples, missing baseline clinicopathological features and incomplete follow-up data. Finally, a total of 1287 NSCLC patient samples were enrolled. To reduce the batch effects, the “ComBat” algorithm was performed as previously prescribed[ 14 ]. Consensus Clustering for Immune Related LncRNAs After NSCLC patient datasets from TCGA were randomly divided into training cohort and validation cohort based on 7:3 ratio followed by GEO validation cohort, unsupervised hierarchical clustering methods (K-means)[ 15 ] were used to identify three immune related lncRNA clusters, corresponding to high, median and low immune groups. These procedures were repeated for 10 000 times to ensure stability. The random forest classification algorithm was performed by using randomForest R package[ 16 ] for the validation. We applied ESTIMATE[ 17 ] directly to bulk expression profiles to derive Stromal, Immune, and ESTIMATE scores and infer tumor purity; heatmaps depict score distributions across lncRNA-based clusters. Tumor-Infiltrating Immune Cells Characteristic Signature ssGSEA algorithm of Gene Set Variation Analysis (GSVA) R package was used to quantify the infiltration level in NSCLC samples of 26 immune cell phenotypes. Additionally, heatmaps were generated to show the distribution and expression levels of tumor-infiltrating immune cells and immune checkpoints in 3 identified clusters based on immune related lncRNAs. Differences on immune cell infiltration and checkpoints among 3 groups were compared through Kruskal-Wallis test. Transcriptome Analysis Weighted Gene Co-expression Network Analysis (WGCNA) R package was utilized to determine mRNA expression association. Potential biological pathways that immune related lncRNAs might involve in were explored via Gene Ontology (GO) Pathway Enrichment Analysis. Protein-protein interaction (PPI) networks analysis was conducted to present visualized related proteins interaction through STRING database ( https://string-db.org/ ) and Cytoscape. Genomic Landscape Characterization The genomic landscape of NSCLC and mutational signatures were presented by oncoplot as previously described[ 18 ]. Comparison of single nucleotide variation (SNV) and tumor mutational burden (TMB) among three identified clusters were performed through Kruskal-Wallis test. Chemotherapeutic and Immunotherapeutic Response Prediction The response to chemotherapy for each case was predicted for the basis of the Genomics of Drug Sensitivity in Cancer (GDSC) database ( https://www.cancerrxgene.org/ )[ 19 , 20 ]. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm mapping was applied to predict the therapeutic response to ICIs[ 21 ]. Statistical Analysis All statistical analyses were conducted by R software version 4.0.1 (R Project for Statistical Computing). χ2 test or Fisher’s exact test was performed for categorical variables. Wilcoxon rank sum test and Kruskal-Wallis test were used for 2-group and multi-group comparisons, respectively. Kaplan-Meier survival plots were utilized for overall survival (OS) estimation. two-sided p value < 0.05 was considered statistically significant. Group comparisons were performed using the Wilcoxon rank-sum test (two groups) and the Kruskal–Wallis test (≥ 3 groups), two-sided unless stated otherwise. Multiple testing in enrichment and gene-set analyses was controlled using the Benjamini–Hochberg false discovery rate (FDR), with q < 0.05 considered significant. Survival curves were estimated by Kaplan–Meier with log-rank tests. Where applicable, effect sizes were summarized as hazard ratios (HR) with 95% confidence intervals from Cox models; proportional hazards assumptions were assessed via Schoenfeld residuals. Training and validation cohorts were strictly separated to avoid information leakage. Results Consensus Clustering and Validation for lncRNAs and Identification of Correlations between Clusters and Clinical Features 1034 immune related lncRNAs were screened out by taking the intersection in the TCGA dataset, GEO dataset and the Immlnc database. Our study recruited GSE30219 (n=145), GSE50081 (n=172) and TCGA-NSCLC dataset (n=970), of which TCGA dataset were divided into training cohort (n=664) and internal validation cohorts (n=306) as 7:3. Based on these immune related lncRNAs, three distinct modification patterns in TCGA training cohort were eventually identified using unsupervised clustering, including 226, 138 and 300 samples in cluster A, B and C respectively (Fig. 1A, Fig. S1A). We observed that the major pathology in cluster C were lung squamous cell carcinoma (LUSC) while the most common pathological types in cluster A and B were lung adenocarcinoma (LUAD). Besides, it is suggested that three immune related lncRNA clusters were characterized by uneven expression of genes (Fig. 1A). By utilizing the random forest algorithm based on the characteristics of the TCGA training group, we divided individuals into three clusters in the internal validation cohort, GSE30219 and GSE50081 cohorts, with a corresponding accuracy over 0.95 (Fig. S1C). According to the result of random forest analysis, we found that four lncRNAs, including MIR205HG , NKX2.1.AS1 , LINC00958 and LINC00668 , played the most important role in our lncRNA clustering (Fig. S1C). Meanwhile, we found that cluster A had the best prognosis by Kaplan-Meier analysis and statistically significant differences were shown among cluster A, B and C (p=0.0048; Fig. 1B). Identical results of survival analysis were figured out in internal validation cohort (p=0.021, Fig. 1C), GSE30219 (p=0.043, Fig. 1D) and GSE50081 (p=0.0014, Fig. 1E) datasets, which showed the patients in the cluster A subgroup had a significantly more favorable prognosis than those in other clusters. The Landscape of Immune Infiltration in the TME of NSCLC After identifying three clusters with significant survival differences, we described the landscape of immune infiltration in different clusters to further verify the potential biological mechanism which may lead to different clinical outcome. With a favorable prognosis, cluster A was marked by distinct infiltration of activated B cell, activated dendritic cell, CD56dim natural killer cell (NK cell), activated CD8 T cell, central memory CD4 T cell, effector memory CD8 T cell, eosinophil, immature dendritic cell, immature B cell, macrophage, myeloid-derived suppressor cells (MDSC) and mast cell. In cluster B, the density of plasma cells, M1 and M2 macrophages, CD8 T cells, gamma delta T cells and memory CD4 T cells infiltration was significantly increased. Cluster C had the lowest existence of immune infiltrating lymphocytes (Fig. 2A). Besides, the heatmap of the correlation between three clusters was generated to describe the universal landscape of interaction among immune cell and human lymphocyte antigen (HLA) and checkpoint molecules in TME (Fig. S2). Cluster A, among the immune-active class, have higher CTL infiltration as well as higher expression of immune molecules than cluster B and C. Cluster C, with a functional immune response but the lowest CTL infiltration, could be the immune-desert class. (Fig. 2B). According to ESTIMATE algorithm, the immune score and estimate score of cluster A was higher than that of cluster B and C (Fig. S3A). ICIs have been proved to be a promising strategy of NSCLC therapy. We subsequently investigated the expression of crucial immunomodulators in three clusters, such as PD-1, CTLA-4, CD28, and TNFRSF14, that higher expression of immune checkpoint molecules was observed in cluster A compared with others (Fig. 2C-F, Fig. S3B-F). Comprehensive Analysis of Molecular and Immune Characteristics in Three Clusters GSVA enrichment analysis was applied to investigate the biological mechanism among these clusters. We observed that cluster A was enriched in immune activation pathways including arachidonic-acid metabolism, intestinal immune network, complement and coagulation cascades. Cluster B was markedly enriched in carcinogenic activation pathways, such as cell adhesion molecules cams, mismatch repair, and MAPK signaling pathways (Fig. 3A, Fig. S4A). While Cluster C was significantly correlated to immune suppression biological process (Fig. 3B, Fig. S4B). Significant pathways among three clusters were presented in Fig. 3C. To obtain the immune related hub genes, WGCNA analysis was carried out on the candidate genes (n = 4000). The log(k) of the node with connectivity K was negatively correlated with the log(P(k)) of the probability of the node, and the correlation coefficient was greater than 0.9 (Fig. S5A). The optimal soft-thresholding power was 6 based on the scale-free network (Fig. S5B). A total of 4000 genes were allocated to eleven modules and identified based on the average linkage hierarchical clustering and the optimal soft-thresholding power (Fig. S5C-E). According to the Pearson correlation coefficient between a module and sample feature for each module, the greenyellow, salmon, and blue modules were closely correlated with 3 clusters. The genes in these modules were selected for further analysis. The correlation analysis of gene co-expression module and clusters demonstrated that the co-expression greenyellow module was significantly associated with cluster A (R=0.57, p<0.001); the co-expression salmon module was significantly associated with cluster B (R=0.22, p<0.001); the co-expression blue module was significantly associated with cluster C (R=0.90, p<0.001)(Fig. 3D). The most significantly enriched GO pathways for the genes in the greenyellow, salmon and blue modules were shown separately in Fig. 3E-G. And it was found that they were significantly enriched in p38 MAPK cascade, regulation of p38 MAPK cascade pathways, growth factor binding and transforming growth factor beta binding in cluster A. We discovered that genes associated with cluster B were mainly enriched in type I interferon signaling pathway, response to type I interferon, response to interferon−gamma, antigen processing and presentation of peptide antigen via MHC class I and MHC class I protein binding and so on. And the genes for cluster C were linked to epidermis development, neutrophil activation involved in immune response, cell adhesion mediator activity and exopeptidase activity. There were 62 genes and 41 edges in the greenyellow module, 60 genes and 485 edges in the salmon module and 613 genes and 2000 edges in the blue module of the networks with a threshold weight > 0.4. The most significant genes in the cluster A were VWF , PECAM1 , SFTP D , AGER . And other hub genes like A2M , CFD , CLEC14A , RGS5 , EPAS1 , CD93 , PLVAP were also showed in the Fig. 3H. The hub genes in cluster B like STAT1 , PARP9 , TRIM21 , OAS3 and so on were showed in the Fig. 3I. The cluster C comprising SOX2 , ZNF281 , KRT19 , MYC , KRT5 , HRAS , CD44 , TP63 and other hup genes were showed in the Fig. 3J. Somatic Mutation of Immune Related lncRNA Clusters We evaluated the distribution differences of somatic variation among these three clusters by using “maftools” R package. The top 30 driving genes with the highest frequency of mutation were further analyzed. Cluster A was characterized by frequent mutation of KRAS , CSMD1 , ANK2 , TNR (Fig. 4A). Cluster B had high mutation of KEAP1 , LRRC7 , PAPPA2 , ABCA13 , APOB , MUC17 , NRXN1 , DNAH9 , SORCS1 and ZNF804A (Fig. 4B). Cluster C showed a significantly increased mutation frequency of SYNE1 , FAM135B and KMT2D (Fig. 4C). The TP53 mutation rate was the lowest in cluster A (38%), with the highest rate in cluster C (80%). Considering the clinical value of TMB, we explored the underlying correlation between the clusters and TMB. As shown in Fig. 4C, TMB in cluster A was lowest (Kruskal-Wallis test, p < 0.001), with highest TMB in the cluster B (Fig. 4D). We also found the same results in the TCGA validation cohort (Kruskal-Wallis test, p < 0.001, Fig. 4E). We further analyzed the level of MSI in the three groups. We found that MSI was the lowest in cluster B (Kruskal-Wallis test, p < 0.001, Fig. 4F), which had been verified in the TCGA validation cohort (Kruskal-Wallis test, p < 0.001, Fig. 4G). The Sensitivity to Immunotherapies, Targeted therapies, and Chemotherapies for Immune Related LncRNA Clusters We used TIDE to assess the potential clinical benefit of ICIs in different clusters. In our results, the cluster A had the lowest TIDE score (p<0.001, Fig. 5A). Also, we found that the cluster B had the higher T cell exclusion score than cluster A (p<0.001, Fig. 5B). Then, we used the TIDE algorithm to predict the probability of response to immunotherapy. We discovered that patients in cluster A might be more likely to respond to immunotherapy than those in cluster B and C (p<0.001, Fig. 5C). Meanwhile, we can observe the same results in the internal validation group (Fig. 5D-F). Considering that chemotherapy and targeted therapy are traditional treatment of NSCLC, we attempted to assess the response to anti-tumor drugs among three clusters. Therefore, we used ridge regression to train the prediction model on the GDSC cell line data set and obtained satisfactory prediction accuracy through 10-fold cross-validation. Based on the prediction models of these six drugs, including cisplatin, docetaxel, NVP.TAE684 (Crizotinib), PF.02341066 (ALK Inhibitor), ABT.888 (Veliparib) and ABT.263 (Navitoclax), we estimated the IC50 of each case in the TCGA dataset. We observed that there was a significant difference in the estimated IC50 of 3 clusters in cisplatin and docetaxel, in which cluster C was predicted to be more sensitive to conventional chemotherapy (p<0.001 for Cisplatin and p<0.001 for Docetaxel, Fig. 5G). However, pertaining to NVP.TAE684 and PF.02341066, cluster A and cluster B showed predicted sensitivity than cluster C (p<0.001 for NVP.TAE684 and p<0.001 for PF.02341066, Fig. 5G). For PARP inhibitor ABT.888 (Veliparib), cluster A and cluster B were also more sensitive than cluster C. And cluster C is more sensitive to Bcl-2 Inhibitor ABT.263 (Navitoclax) (p<0.001, Fig. 5G). Discussion In our study, unsupervised clustering analysis of immune-related lncRNAs has been employed to identify novel subtypes of NSCLC with distinct immune microenvironments. We conducted a comprehensive analysis of these differences, including genomic characterization, immune checkpoint expression, TMB, responses to chemotherapy, targeted therapy, and immunotherapy. Our results suggest that lncRNA-based TME classification may complement immune checkpoints and tumor mutation burden as predictive biomarkers for immunotherapy response, but prospective clinical validation is required. Recently, mounting evidence indicates that lncRNA plays a significant role in the regulation of the immune system, inflammation and anti-tumor effect[ 22 , 23 ]. Most research has focused on the mediation of the TME by single lncRNAs affecting specific cell types or pathways. However, the TME, which is mediated by the comprehensive interaction of multiple lncRNAs, has not been fully understood. Identifying distinct lncRNA signatures in TME cell infiltration is crucial for deepening our understanding of TME anti-tumor responses and directing more tailored and accurate immunotherapeutic strategies. Herein, we unveiled three distinct NSCLC immune-related lncRNA molecular patterns with significantly different TME cell infiltration characteristics. Cluster A is characterized by abundant B and T cells with high expression of HLA (human leukocyte antigen), corresponding to an immune-inflamed phenotype. This immune-inflamed phenotype, known as a 'hot tumor,' is characterized by abundant immune cell infiltration in the TME[ 24 – 26 ]. Some research demonstrates the underlying mechanisms for T cell and immune system activation, wherein B cells promote T cell activation and survival against tumors. Another mechanism involves the activation of HLA-LOH (loss of heterozygosity) as a form of immunoediting to deprive immune cells of antigen presentation, subsequently activating T cells[ 27 ]. Additionally, cluster A is found to have the highest expression of PD-1, CD28 and CTLA4. Alegre et al. recently discover that the inhibition of CTLA-4 interferes with the normal function of Treg cells through CD28 signal transduction, enhancing anti-tumor immunity in melanoma[ 28 ]. Our results in NSCLC are consistent with these findings. The interference is more pronounced in tumors with low-level glycolysis, and the efficacy of CTLA-4 inhibitors may be enhanced when combined with glycolysis inhibitors[ 29 ]. Cluster B and C are respectively characterized by escape from the immune system and suppression of immunity, corresponding to the immune-evaded phenotype and immune-desert phenotype. Both the immune-evaded phenotype and immune-desert phenotype are regarded as non-inflammatory tumor types[ 30 ]. Although abundant immune cell infiltration in the TME also occurs in the immune-excluded phenotype, immune cells are predominantly located in the stroma around the tumor rather than infiltrating the parenchyma[ 31 – 33 ]. The immune-desert phenotype is characterized by immune tolerance and a scarcity of inflammatory factors that activate and initiate T cells[ 34 ]. In our analysis of immune infiltration across distinct clusters, we observed comparable levels of immune cell infiltration in clusters A and B, yet discrepancies emerged in immune scores and prognosis. To delve into these differences, we conducted a comprehensive examination at the transcriptional level through WGCNA and GO. Additionally, we assessed the distribution of somatic variations among these clusters using the "maftools" R package. Cluster A exhibited enrichment in both the p38 MAPK cascade and regulation of p38 MAPK cascade pathways. Previous studies have underscored the significant activation of p38 MAPK in diverse cell types within the TME[ 35 ], influencing the expression of extracellular factors such as VEGFA, IL8, and HBEGF, thereby promoting angiogenesis[ 36 ]. Additionally, as one of the hub genes in cluster A, VWF is a polymer coagulation plasma glycoprotein and mediates platelet adherence along endothelial cells[ 37 ]. Recent research depicts its biological function including inflammation and angiogenesis[ 38 ], which may participate in immune cell enrichment shown in cluster A. Moreover, the somatic mutation analysis revealed a relatively high incidence of KRAS mutations in Cluster A. KRAS mutation is as prevalent as EGFR mutation while NSCLC patients with KRAS mutation responses poorly to chemotherapy and no effective inhibitors for KRAS mutation for the moment[ 39 ]. Intriguingly, recent research suggests that KRAS -mutant cases exhibit increased sensitivity to PD-1/PD-L1 inhibitors[ 40 ], implying a potential responsiveness to immunotherapy in Cluster A. Cluster B, on the other hand, demonstrated enrichment in IFN-γ and IFN-γ-related MHC class I pathways. Despite IFN-γ being recognized as an anti-tumor cytokine, it plays a dual role. Firstly, it serves as a marker of anti-tumor immunity. Secondly, it acts as an inducer of immune escape phenomena through multiple mechanisms. Recent studies show that IFN upregulates both classical and non-classical MHC class I genes. This process inhibits NK cells and CD8 + T cells, leading to immune evasion, consistent with our findings in NSCLC[ 41 ]. PPI analysis highlighted a significant association with STAT1 in Cluster B, a key regulator in infection and inflammatory cell development[ 42 ]. STAT1 plays a critical role in immune dysregulation, and elevated levels of STAT1 expression have been reported to correlate with poorer OS in kidney cancer, low-grade glioma, lung adenocarcinoma, and pancreatic cancer[ 43 ]. This interpretation suggests that, from another perspective, the majority of immune cells in cluster B are in a state of immune dysregulation. In relation to the somatic mutation rate, our analysis revealed a relatively high mutation rate of KEAP1 in cluster B. This finding aligns with the observations of Hellyer et al., who reported that mutations in KEAP1 - NFE2L2 are significant regulators of cellular homeostasis, with mutations correlating to increased tumor growth and invasiveness—a phenomenon commonly observed in NSCLC[ 44 ]. Elizabeth et al. reported that despite a relatively high TMB, LUAD driven by KEAP1 mutation shows a limited response to immunotherapy [ 45 ]. In our study, cluster B is distinguished by the highest TMB and the presence of KEAP1 mutations, traits typically associated with resistance to immunotherapy and indicative of immune evasion within this cluster. Consequently, it is imperative for us to delve deeper into understanding the interplay between the KEAP1 driver gene and IFN-γ, along with the IFN-γ-related MHC class I pathway. Furthermore, an exploration of the mechanisms underpinning immune evasion in cluster B is warranted. In Cluster C, the notable association with SOX2 has captured our attention. SOX2 functions as a transcription factor and plays a crucial role in various stages of embryonic development, influencing processes such as cell fate determination and differentiation[ 46 ]. SOX2 has been identified as highly expressed in LUSC, which constitutes the predominant pathological subtype in cluster C. Recent reports have indicated that overexpression and gene amplification of SOX2 are associated with tumor invasion and metastasis in various cancers[ 47 ]. Studies have demonstrated that the interaction between SOX2 and both wild-type NSD3 and the activity-enhancing mutant NSD3T1232A can induce oncogenic transformation in human tracheobronchial epithelial cells (AALE), consequently altering the TME[ 48 ]. This could contribute to an immune-desert phenotype and a poor prognosis in cluster C. Our findings indicate that the mutation rate of TP53 was the highest in this cluster, while the TMB was lower. Recent research has reported that Mutant p53 (Mtp53) can induce carcinogenesis through the cGAS-STING-TBK1-IRF3 pathway, suppressing both cell-autonomous and non-cell-autonomous signaling to favor cancer cell survival and escape from immune surveillance[ 49 ]. Cluster C exhibits an immune-desert phenotype characterized by a lower TMB, suggesting a poorer response to immunotherapy and an unfavorable prognosis. We also observed that clusters A and B are predominantly composed of LUAD, while cluster C is primarily LUSC. Cluster A exhibits the highest immune cell infiltration. Notably, the TIDE score, which has been demonstrated to more accurately predict the prognosis of patients with melanoma after anti-PD-1 or anti-CTLA4 treatment than other indicators such as PD-L1 and TMB, was lower in cluster A. Additionally, cluster A showed a higher proportion of responders to immunotherapy compared to cluster C, suggesting that LUAD may respond more favorably to immunotherapy than LUSC. This observation has potential clinical implications: LUAD patients, particularly those with Cluster A features, could be prioritized for immunotherapy trials, while LUSC patients (Cluster C) might benefit more from chemotherapy-based regimens. Such subtype-guided stratification could refine treatment decisions in NSCLC. Utilizing the GDSC database, we inferred that cluster C is more sensitive to commonly used NSCLC chemotherapy, while clusters A and B prefer small molecule targeted drugs. This finding aligns with recent studies indicating that LUSC is sensitive to chemotherapy, while LUAD responds better to targeted therapy. Limitations However, several limitations exist in our study. Firstly, our analysis relies on TCGA and GEO databases, lacking real-world immunotherapy cohorts for validating the predictions of immunotherapy efficacy. Secondly, while we constructed lncRNA molecular patterns and validated them through biostatistical analysis, the underlying mechanisms driving the discrepancies in these lncRNA molecular patterns warrant further investigation through experimental studies. Thirdly, our exploration of therapeutic sensitivity is focused on a limited number of drugs within the context of our lncRNA molecular patterns, and additional studies are needed to expand the clinical implications for a broader range of drugs. Moreover, all therapy and immunotherapy findings are computational predictions and should be interpreted cautiously until validated in experimental or clinical settings. Conclusion In conclusion, our study establishes three distinct molecular patterns based on immune-related lncRNAs in NSCLC, each characterized by unique phenotypic features. We unravel potential mechanisms by which immune-related lncRNAs influence the TME. These lncRNA patterns are not only associated with the prognosis of NSCLC patients but also demonstrate predictive value for clinical responses to chemotherapy, targeted therapy, and immunotherapy. Significantly, our findings provide novel insights into immunotherapy responses and may complement conventional concepts of hot and cold tumors. These observations suggest potential implications for guiding immunotherapy or drug combination strategies, pending further validation. Furthermore, our study holds promise for identifying distinct tumor immune phenotypes, enhancing clinical responses to immunotherapy, and paving the way for tailored immunotherapeutic approaches in the future. Declarations Author Contributions Conceptualization, Ang Li and Yutao Pang; Writing, Ang Li; Visualization, Ang Li, Yutao Pang, Xiao Yang, Hongfei Zhang, Dong Wu, Liyao Lin, Zhan He and Zhu Liang ; Funding Acquisition, Jie Chen and Fasheng Li. All authors have read and agreed to the published version of the manuscript. Funding This project was supported by the Key Clinical Projects of Affiliated Hospital of Guangdong Medical University (LCYJ2022DL003); the supported projects of Zhanjiang (2021A05076). Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Clinical trial number Not applicable. Data Availability Statement The datasets analyzed in this study are publicly available. TCGA-LUAD and TCGA-LUSC datasets were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). GSE30219(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30219), GSE50081 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081), GPL570 platform annotation (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL570) were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Processed data and analysis scripts are available from the corresponding author on reasonable request. Acknowledgements We express our gratitude for the valuable data provided by TCGA and GEO. Our sincere thanks go to all individuals involved in these initiatives. 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1","display":"","copyAsset":false,"role":"figure","size":473222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus clustering for lncRNA and survival analysis of immune-related lncRNA based clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Unsupervised clustering of immune-related lncRNA in training cohort and internal validation cohort. (B) Survival analysis of immune-related lncRNA based clusters in training cohort. (p=0.0048) (C) Survival analysis of immune-related lncRNA based clusters in internal validation cohort. (p=0.021) (D) Survival analysis of immune-related lncRNA based clusters in GSE30219. (p=0.043) (E) Survival analysis of immune-related lncRNA based clusters in GSE50081. (p=0.014) Survival analysis is conducted by Kaplan-Meier analysis. Abbreviation: Lung adenocarcinoma, LUAD. Lung Squamous Cell Carcinoma, LUSC. Tumor stage is evaluated according to the eighth edition of American Joint Committee on Cancer staging manual.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/ee9222a90a33657f696af12a.png"},{"id":95650728,"identity":"0aa99844-312a-4a46-b809-8b09a1a4d2a4","added_by":"auto","created_at":"2025-11-11 15:14:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of tumor immune microenvironment in lncRNA based clusters. \u003c/strong\u003e(A) Tumor infiltrating immune cell analysis through single sample gene set enrichment analysis. (B) Comparison of tumor infiltrating immune cells, expression of immune checkpoint and HLA among immune-related lncRNA based clusters. (C-F) The expression of immune checkpoints including PD-1, CTLA-4, CD28, TNFRSF14 among immune-related lncRNA based clusters. (Kruskal-Wallis test, p\u0026lt;0.001 for PD-1, CTLA-4, CD28, TNFRSF14) Abbreviation: Human Leukocyte Antigen, HLA. Myeloid-derived suppressor cells, MDSC. Activated T cells, T\u003csub\u003eact\u003c/sub\u003e cells. Central Memory T cells, T\u003csub\u003ecen/mem \u003c/sub\u003ecells. Effector Memory T cells, T\u003csub\u003eeffe/mem \u003c/sub\u003ecells. Regulatory T cells, Treg. Follicular Helper T cells, Tfh. T Helper cells, Th cells. Dendritic cells, DCs. Nature Killer cells, NK cells.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/7bfab9cf3db9f3514e19bf42.png"},{"id":95657660,"identity":"6c617d48-59b1-4b08-bb4d-fa93e845382f","added_by":"auto","created_at":"2025-11-11 16:21:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular and pathway analysis in immune-related lncRNA based clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Significant dysregulated pathways by GSVA enrichment analysis between cluster A and cluster B. (B) Significant dysregulated pathways by GSVA enrichment analysis between cluster A and cluster C. (C) Differences of hub pathways in carcinogenesis among three clusters. (D) Module-trait relationship of WGCNA. (E-G) GO functional analysis of genes correlated most with clusters. (H-J) Hub genes of genes correlated most with clusters by protein-protein interaction. Kruskal-Wallis test, \u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt;0.01, \u003csup\u003e***\u003c/sup\u003ep \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/00b1137b92168f9bcad6abd3.png"},{"id":95657998,"identity":"dc66e32e-f36b-48b3-bc5a-333742cc8908","added_by":"auto","created_at":"2025-11-11 16:22:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of immune-related lncRNA based clusters in gene instability and tumor somatic mutation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-C) The waterfall plot of tumor somatic mutation established by three clusters. (D-E) Tumor mutation burden in training cohort and internal validation cohort among three clusters. (F-G) Microsatellite instability in training cohort and internal validation cohort among three clusters. Abbreviation: Tumor Mutation Burden, TMB. Microsatellite instability, MSI. Kruskal-Wallis test, \u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt;0.01, \u003csup\u003e***\u003c/sup\u003ep \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/b42011fe66a0c7743bc9bb35.png"},{"id":95650737,"identity":"5c8ee4ad-1916-43a6-9bf8-0fcdcdf12ceb","added_by":"auto","created_at":"2025-11-11 15:14:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe sensitivity to immunotherapy, targeted therapy, and chemotherapy for immune related lncRNA clusters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A and D) TIDE scores of training cohort and internal validation cohort. (B and E) Exclusion scores from TIDE analysis in training cohort and internal validation cohort. (C and F) Predicted responders for immunotherapy by TIDE analysis in training cohort and internal validation cohort. (G) Drug sensitivity analysis through GDSC for targeted therapy and chemotherapy (Cisplatin, Docetaxel, NVP.TAE684, PF.02341066, ABT.888, ABT.263). Abbreviation: Tumor Immune Dysfunction and Exclusion, TIDE. Genomics of Drug Sensitivity in Cancer, GDSC. Kruskal-Wallis test, \u003csup\u003e*\u003c/sup\u003ep\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt;0.01, \u003csup\u003e***\u003c/sup\u003ep \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/9129fc67c036e4ddf2e30266.png"},{"id":95660771,"identity":"d6da651d-67a2-43ff-8908-d30a6e64b370","added_by":"auto","created_at":"2025-11-11 16:32:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3881880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/12fdfdea-a194-42ea-a2c7-24f401363c53.pdf"},{"id":95650735,"identity":"768b96d5-bbd5-49a9-bf67-d4e30243c36c","added_by":"auto","created_at":"2025-11-11 15:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1464765,"visible":true,"origin":"","legend":"","description":"","filename":"PublicationreadySupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7575439/v1/6a2f6883cbf94db9259fe919.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune-Related lncRNA Signatures Define Tumor Microenvironment Subtypes and Predict Immunotherapy Response in NSCLC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer holds the highest incidence and mortality rates worldwide, with non-small cell lung cancer (NSCLC) constituting approximately 85% of cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in early diagnosis and treatment, which include surgery, radiochemotherapy, and targeted therapy, the 5-year overall survival rate for patients with NSCLC remains less than 30%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, continued research focused on molecular biomarkers and novel therapies is crucial for prognosis prediction and individualized treatment in NSCLC.\u003c/p\u003e\u003cp\u003eOver the last decade, the introduction of immune checkpoint inhibitors (ICI) such as programmed cell death 1 (PD-1) or programmed cell death ligand 1 (PD-L1) inhibitors has transformed the management of NSCLC[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, immunotherapy is not suitable for all patients, with only about 30% of lung cancer patients experiencing benefits from this form of treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Various biomarkers, including PD-L1 expression, tumor mutation burden (TMB), and microsatellite instability (MSI), have been reported to predict the response of solid tumors to immunotherapy, albeit with average prediction ability[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith a length longer than 200 nucleotides, long non-coding RNAs (lncRNAs) do not encode proteins but participate in various cellular processes, including cell proliferation, differentiation, apoptosis, and metastasis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, growing research has demonstrated that lncRNAs emerge as critical regulators of the immune system by directing the expression of immune-related genes at epigenetic, transcriptional, and post-transcriptional levels[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, the lncRNA NKILA has been found to regulate T cell sensitivity to activation-induced cell death in lung cancer microenvironments[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The lncRNA lincRNA-Cox2 has been demonstrated to mediate the activation and suppression of diverse immune genes to control the inflammatory response[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another lncRNA, LINK-A, has been proven to impair cancer cell antigen presentation and intrinsic tumor suppression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Collectively, these studies indicate that lncRNAs serve as vital regulators in tumor immunology.\u003c/p\u003e\u003cp\u003eMore recently, increasing evidence indicates that the tumor microenvironment (TME), consisting of a mixture of tumor cells with tumor-infiltrating immune cells and stromal components, plays a critical role in tumor proliferation, invasion, and metastasis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Interactions between immune cells and cytokines in the TME modulate the response to immunotherapy.\u003c/p\u003e\u003cp\u003eIn this study, we hypothesized that lncRNAs could shape the tumor microenvironment and modulate the response to immunotherapy. We subsequently established three clinically relevant NSCLC subtypes based on immune-related lncRNAs. Through analysis of immune infiltration, pathway analysis, and driver genes, we aimed to explore the similarities and differences between each subtype. Additionally, we analyzed the sensitivity of different subtypes to immunity, targeted therapy, and chemotherapy. These findings are expected to contribute to the diagnosis and treatment of non-small cell lung cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDataset Source Retrieval and Preprocessing\u003c/h2\u003e\u003cp\u003eNSCLC sample datasets with transcriptional profiles data (GSE30219, GSE50081, GPL570) were obtained from the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed June 2020) and The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed June, 2020). NSCLC Patients with clinical stage I-III were included in this study. Exclusion criteria include mixed histology, inadequate or poor-quality samples, missing baseline clinicopathological features and incomplete follow-up data. Finally, a total of 1287 NSCLC patient samples were enrolled. To reduce the batch effects, the \u0026ldquo;ComBat\u0026rdquo; algorithm was performed as previously prescribed[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eConsensus Clustering for Immune Related LncRNAs\u003c/h3\u003e\n\u003cp\u003eAfter NSCLC patient datasets from TCGA were randomly divided into training cohort and validation cohort based on 7:3 ratio followed by GEO validation cohort, unsupervised hierarchical clustering methods (K-means)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] were used to identify three immune related lncRNA clusters, corresponding to high, median and low immune groups. These procedures were repeated for 10 000 times to ensure stability. The random forest classification algorithm was performed by using randomForest R package[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for the validation.\u003c/p\u003e\u003cp\u003eWe applied ESTIMATE[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] directly to bulk expression profiles to derive Stromal, Immune, and ESTIMATE scores and infer tumor purity; heatmaps depict score distributions across lncRNA-based clusters.\u003c/p\u003e\n\u003ch3\u003eTumor-Infiltrating Immune Cells Characteristic Signature\u003c/h3\u003e\n\u003cp\u003essGSEA algorithm of Gene Set Variation Analysis (GSVA) R package was used to quantify the infiltration level in NSCLC samples of 26 immune cell phenotypes. Additionally, heatmaps were generated to show the distribution and expression levels of tumor-infiltrating immune cells and immune checkpoints in 3 identified clusters based on immune related lncRNAs. Differences on immune cell infiltration and checkpoints among 3 groups were compared through Kruskal-Wallis test.\u003c/p\u003e\n\u003ch3\u003eTranscriptome Analysis\u003c/h3\u003e\n\u003cp\u003eWeighted Gene Co-expression Network Analysis (WGCNA) R package was utilized to determine mRNA expression association. Potential biological pathways that immune related lncRNAs might involve in were explored via Gene Ontology (GO) Pathway Enrichment Analysis. Protein-protein interaction (PPI) networks analysis was conducted to present visualized related proteins interaction through STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Cytoscape.\u003c/p\u003e\n\u003ch3\u003eGenomic Landscape Characterization\u003c/h3\u003e\n\u003cp\u003eThe genomic landscape of NSCLC and mutational signatures were presented by oncoplot as previously described[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Comparison of single nucleotide variation (SNV) and tumor mutational burden (TMB) among three identified clusters were performed through Kruskal-Wallis test.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eChemotherapeutic and Immunotherapeutic Response Prediction\u003c/h2\u003e\u003cp\u003eThe response to chemotherapy for each case was predicted for the basis of the Genomics of Drug Sensitivity in Cancer (GDSC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm mapping was applied to predict the therapeutic response to ICIs[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted by R software version 4.0.1 (R Project for Statistical Computing). χ2 test or Fisher\u0026rsquo;s exact test was performed for categorical variables. Wilcoxon rank sum test and Kruskal-Wallis test were used for 2-group and multi-group comparisons, respectively. Kaplan-Meier survival plots were utilized for overall survival (OS) estimation. two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eGroup comparisons were performed using the Wilcoxon rank-sum test (two groups) and the Kruskal\u0026ndash;Wallis test (\u0026ge;\u0026thinsp;3 groups), two-sided unless stated otherwise. Multiple testing in enrichment and gene-set analyses was controlled using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR), with q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Survival curves were estimated by Kaplan\u0026ndash;Meier with log-rank tests. Where applicable, effect sizes were summarized as hazard ratios (HR) with 95% confidence intervals from Cox models; proportional hazards assumptions were assessed via Schoenfeld residuals. Training and validation cohorts were strictly separated to avoid information leakage.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eConsensus Clustering and Validation for lncRNAs and Identification of Correlations between Clusters and Clinical Features\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1034 immune related lncRNAs were screened out by taking the intersection in the TCGA dataset, GEO dataset and the Immlnc database. Our study recruited GSE30219 (n=145), GSE50081 (n=172) and TCGA-NSCLC dataset (n=970), of which TCGA dataset were divided into training cohort (n=664) and internal validation cohorts (n=306) as 7:3. Based on these immune related lncRNAs, three distinct modification patterns in TCGA training cohort were eventually identified using unsupervised clustering, including 226, 138 and 300 samples in cluster A, B and C respectively (Fig. 1A, Fig. S1A). We observed that the major pathology in cluster C were lung squamous cell carcinoma (LUSC)\u0026nbsp;while the most common pathological types in cluster A and B were lung adenocarcinoma (LUAD). Besides, it is suggested that three immune related lncRNA clusters were characterized by\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003euneven expression of genes (Fig. 1A). By utilizing the random forest algorithm based on the characteristics of the TCGA training group, we divided individuals into three clusters in the internal validation cohort, GSE30219 and GSE50081 cohorts, with a corresponding accuracy over 0.95 (Fig. S1C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the result of random forest analysis, we found that four lncRNAs, including \u003cem\u003eMIR205HG\u003c/em\u003e, \u003cem\u003eNKX2.1.AS1\u003c/em\u003e, \u003cem\u003eLINC00958\u003c/em\u003e and \u003cem\u003eLINC00668\u003c/em\u003e, played the most important role in our lncRNA clustering (Fig. S1C). Meanwhile, we found that cluster A had the best prognosis by Kaplan-Meier analysis and statistically significant differences were shown among cluster A, B and C (p=0.0048; Fig. 1B). Identical results of survival analysis were figured out in internal validation cohort (p=0.021, Fig. 1C), GSE30219 (p=0.043, Fig. 1D) and GSE50081 (p=0.0014, Fig. 1E) datasets, which showed the patients in the cluster A subgroup had a significantly more favorable prognosis than those in other clusters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Landscape of Immune Infiltration in the TME of NSCLC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter identifying three clusters with significant survival differences, we described the landscape of immune infiltration in different clusters to further verify the potential biological mechanism which may lead to different clinical outcome. With a favorable prognosis, cluster A was marked by distinct infiltration of activated B cell, activated dendritic cell, CD56dim natural killer cell (NK cell), activated CD8 T cell, central memory CD4 T cell, effector memory CD8 T cell, eosinophil, immature dendritic cell, immature B cell, macrophage, myeloid-derived suppressor cells (MDSC) and mast cell. In cluster B, the density of plasma cells, M1 and M2 macrophages, CD8 T cells, gamma delta T cells and memory CD4 T cells infiltration was significantly increased. Cluster C had the lowest existence of immune infiltrating lymphocytes (Fig. 2A). Besides, the heatmap of the correlation between three clusters was generated to describe the universal landscape of interaction among immune cell and human lymphocyte antigen (HLA) and checkpoint molecules in TME (Fig. S2). Cluster A, among the immune-active class, have higher CTL infiltration as well as higher expression of immune molecules than cluster B and C. Cluster C, with a functional immune response but the lowest CTL infiltration, could be the immune-desert class. (Fig. 2B). According to ESTIMATE algorithm, the immune score and estimate score of cluster A was higher than that of cluster B and C (Fig. S3A). ICIs have been proved to be a promising strategy of NSCLC therapy. We subsequently investigated the expression of crucial immunomodulators in three clusters, such as PD-1, CTLA-4, CD28, and TNFRSF14, that higher expression of immune checkpoint molecules was observed in cluster A compared with others (Fig. 2C-F, Fig. S3B-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive Analysis of Molecular and Immune Characteristics in Three Clusters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSVA enrichment analysis was applied to investigate the biological mechanism among these clusters. We observed that cluster A was enriched in immune activation pathways including arachidonic-acid metabolism, intestinal immune network, complement and coagulation cascades. Cluster B was markedly enriched in carcinogenic activation pathways, such as cell adhesion molecules cams, mismatch repair, and MAPK signaling pathways (Fig. 3A, Fig. S4A). While Cluster C was significantly correlated to immune suppression biological process (Fig. 3B, Fig. S4B). Significant pathways among three clusters were presented in Fig. 3C.\u003c/p\u003e\n\u003cp\u003eTo obtain the immune related hub genes, WGCNA analysis was carried out on the candidate genes (n = 4000). The log(k) of the node with connectivity K was negatively correlated with the log(P(k)) of the probability of the node, and the correlation coefficient was greater than 0.9 (Fig. S5A). The optimal soft-thresholding power was 6 based on the scale-free network (Fig. S5B). A total of 4000 genes were allocated to eleven modules and identified based on the average linkage hierarchical clustering and the optimal soft-thresholding power (Fig. S5C-E). According to the Pearson correlation coefficient between a module and sample feature for each module, the greenyellow, salmon, and blue modules were closely correlated with 3 clusters. The genes in these modules were selected for further analysis. The correlation analysis of gene co-expression module and clusters demonstrated that the co-expression greenyellow module was significantly associated with cluster A (R=0.57, p\u0026lt;0.001); the co-expression salmon module was significantly associated with cluster B (R=0.22, p\u0026lt;0.001); the co-expression blue module was significantly associated with cluster C (R=0.90, p\u0026lt;0.001)(Fig. 3D). The most significantly enriched GO pathways for the genes in the greenyellow, salmon and blue modules were shown separately in Fig. 3E-G. And it was found that they were significantly enriched in p38 MAPK cascade, regulation of p38 MAPK cascade pathways, growth factor binding and transforming growth factor beta binding in cluster A. We discovered that genes associated with cluster B were mainly enriched in type I interferon signaling pathway, response to type I interferon, response to interferon\u0026minus;gamma, antigen processing and presentation of peptide antigen via MHC class I and MHC class I protein binding and so on. And the genes for cluster C were linked to epidermis development, neutrophil activation involved in immune response, cell adhesion mediator activity and exopeptidase activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere were 62 genes and 41 edges in the greenyellow module, 60 genes and 485 edges in the salmon module and 613 genes and 2000 edges in the blue module of the networks with a threshold weight \u0026gt; 0.4. The most significant genes in the cluster A were \u003cem\u003eVWF\u003c/em\u003e, \u003cem\u003ePECAM1\u003c/em\u003e, \u003cem\u003eSFTP\u003c/em\u003e\u003cem\u003eD\u003c/em\u003e, \u003cem\u003eAGER\u003c/em\u003e. And other hub genes like \u003cem\u003eA2M\u003c/em\u003e, \u003cem\u003eCFD\u003c/em\u003e, \u003cem\u003eCLEC14A\u003c/em\u003e,\u003cem\u003e\u0026nbsp;RGS5\u003c/em\u003e, \u003cem\u003eEPAS1\u003c/em\u003e, \u003cem\u003eCD93\u003c/em\u003e,\u003cem\u003e\u0026nbsp;PLVAP\u003c/em\u003e were also showed in the Fig. 3H. The hub genes in cluster B like \u003cem\u003eSTAT1\u003c/em\u003e, \u003cem\u003ePARP9\u003c/em\u003e, \u003cem\u003eTRIM21\u003c/em\u003e,\u003cem\u003e\u0026nbsp;OAS3\u003c/em\u003e and so on were showed in the Fig. 3I. The cluster C comprising \u003cem\u003eSOX2\u003c/em\u003e, \u003cem\u003eZNF281\u003c/em\u003e,\u003cem\u003e\u0026nbsp;KRT19\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eKRT5\u003c/em\u003e, \u003cem\u003eHRAS\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eTP63\u0026nbsp;\u003c/em\u003eand other hup genes were showed in the Fig. 3J.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSomatic Mutation of Immune Related lncRNA Clusters\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the distribution differences of somatic variation among these three clusters by using \u0026ldquo;maftools\u0026rdquo;\u0026nbsp;R package. The top 30 driving genes with the highest frequency of mutation were further analyzed. Cluster A was characterized by frequent mutation of \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eCSMD1\u003c/em\u003e, \u003cem\u003eANK2\u003c/em\u003e, \u003cem\u003eTNR\u0026nbsp;\u003c/em\u003e(Fig. 4A). Cluster B had high mutation of \u003cem\u003eKEAP1\u003c/em\u003e, \u003cem\u003eLRRC7\u003c/em\u003e, \u003cem\u003ePAPPA2\u003c/em\u003e, \u003cem\u003eABCA13\u003c/em\u003e,\u003cem\u003e\u0026nbsp;APOB\u003c/em\u003e, \u003cem\u003eMUC17\u003c/em\u003e, \u003cem\u003eNRXN1\u003c/em\u003e, \u003cem\u003eDNAH9\u003c/em\u003e,\u003cem\u003e\u0026nbsp;SORCS1\u003c/em\u003e and \u003cem\u003eZNF804A\u003c/em\u003e (Fig. 4B). Cluster C showed a significantly increased mutation frequency of \u003cem\u003eSYNE1\u003c/em\u003e, \u003cem\u003eFAM135B\u003c/em\u003e and \u003cem\u003eKMT2D\u0026nbsp;\u003c/em\u003e(Fig. 4C). The \u003cem\u003eTP53\u003c/em\u003e mutation rate was the lowest in cluster A (38%), with the highest rate in cluster C (80%).\u003c/p\u003e\n\u003cp\u003eConsidering the clinical value of TMB, we explored the underlying correlation between the clusters and TMB. As shown in Fig. 4C, TMB in cluster A was lowest (Kruskal-Wallis test, p \u0026lt; 0.001), with highest TMB in the cluster B (Fig. 4D). We also found the same results in the TCGA validation cohort (Kruskal-Wallis test, p \u0026lt; 0.001, Fig. 4E). We further analyzed the level of MSI in the three groups. We found that MSI was the lowest in cluster B (Kruskal-Wallis test, p \u0026lt; 0.001, Fig. 4F), which had been verified in the TCGA validation cohort (Kruskal-Wallis test, p \u0026lt; 0.001, Fig. 4G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Sensitivity to Immunotherapies, Targeted therapies, and Chemotherapies for Immune Related LncRNA Clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used TIDE to assess the potential clinical benefit of ICIs in different clusters. In our results, the cluster A had the lowest TIDE score (p\u0026lt;0.001, Fig. 5A). Also, we found that the cluster B had the higher T cell exclusion score than cluster A (p\u0026lt;0.001, Fig. 5B). Then, we used the TIDE algorithm to predict the probability of response to immunotherapy. We discovered that patients in cluster A might be more likely to respond to immunotherapy than those in cluster B and C (p\u0026lt;0.001, Fig. 5C). Meanwhile, we can observe the same results in the internal validation group (Fig. 5D-F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering that chemotherapy and targeted therapy are traditional treatment of NSCLC, we attempted to assess the response to anti-tumor drugs among three clusters. Therefore, we used ridge regression to train the prediction model on the GDSC cell line data set and obtained satisfactory prediction accuracy through 10-fold cross-validation. Based on the prediction models of these six drugs, including cisplatin, docetaxel, NVP.TAE684 (Crizotinib), PF.02341066 (ALK Inhibitor), ABT.888 (Veliparib) and ABT.263 (Navitoclax), we estimated the IC50 of each case in the TCGA dataset. We observed that there was a significant difference in the estimated IC50 of 3 clusters in cisplatin and docetaxel, in which cluster C was predicted to be more sensitive to conventional chemotherapy (p\u0026lt;0.001 for Cisplatin and p\u0026lt;0.001 for Docetaxel, Fig. 5G). However, pertaining to NVP.TAE684 and PF.02341066, cluster A and cluster B showed predicted sensitivity than cluster C (p\u0026lt;0.001 for NVP.TAE684 and p\u0026lt;0.001 for PF.02341066, Fig. 5G). For PARP inhibitor ABT.888 (Veliparib), cluster A and cluster B were also more sensitive than cluster C. And cluster C is more sensitive to Bcl-2 Inhibitor ABT.263 (Navitoclax) (p\u0026lt;0.001, Fig. 5G).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, unsupervised clustering analysis of immune-related lncRNAs has been employed to identify novel subtypes of NSCLC with distinct immune microenvironments. We conducted a comprehensive analysis of these differences, including genomic characterization, immune checkpoint expression, TMB, responses to chemotherapy, targeted therapy, and immunotherapy. Our results suggest that lncRNA-based TME classification may complement immune checkpoints and tumor mutation burden as predictive biomarkers for immunotherapy response, but prospective clinical validation is required.\u003c/p\u003e\u003cp\u003eRecently, mounting evidence indicates that lncRNA plays a significant role in the regulation of the immune system, inflammation and anti-tumor effect[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Most research has focused on the mediation of the TME by single lncRNAs affecting specific cell types or pathways. However, the TME, which is mediated by the comprehensive interaction of multiple lncRNAs, has not been fully understood. Identifying distinct lncRNA signatures in TME cell infiltration is crucial for deepening our understanding of TME anti-tumor responses and directing more tailored and accurate immunotherapeutic strategies.\u003c/p\u003e\u003cp\u003eHerein, we unveiled three distinct NSCLC immune-related lncRNA molecular patterns with significantly different TME cell infiltration characteristics. Cluster A is characterized by abundant B and T cells with high expression of HLA (human leukocyte antigen), corresponding to an immune-inflamed phenotype. This immune-inflamed phenotype, known as a 'hot tumor,' is characterized by abundant immune cell infiltration in the TME[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Some research demonstrates the underlying mechanisms for T cell and immune system activation, wherein B cells promote T cell activation and survival against tumors. Another mechanism involves the activation of HLA-LOH (loss of heterozygosity) as a form of immunoediting to deprive immune cells of antigen presentation, subsequently activating T cells[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, cluster A is found to have the highest expression of PD-1, CD28 and CTLA4. Alegre et al. recently discover that the inhibition of CTLA-4 interferes with the normal function of Treg cells through CD28 signal transduction, enhancing anti-tumor immunity in melanoma[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our results in NSCLC are consistent with these findings. The interference is more pronounced in tumors with low-level glycolysis, and the efficacy of CTLA-4 inhibitors may be enhanced when combined with glycolysis inhibitors[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Cluster B and C are respectively characterized by escape from the immune system and suppression of immunity, corresponding to the immune-evaded phenotype and immune-desert phenotype. Both the immune-evaded phenotype and immune-desert phenotype are regarded as non-inflammatory tumor types[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although abundant immune cell infiltration in the TME also occurs in the immune-excluded phenotype, immune cells are predominantly located in the stroma around the tumor rather than infiltrating the parenchyma[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The immune-desert phenotype is characterized by immune tolerance and a scarcity of inflammatory factors that activate and initiate T cells[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our analysis of immune infiltration across distinct clusters, we observed comparable levels of immune cell infiltration in clusters A and B, yet discrepancies emerged in immune scores and prognosis. To delve into these differences, we conducted a comprehensive examination at the transcriptional level through WGCNA and GO. Additionally, we assessed the distribution of somatic variations among these clusters using the \"maftools\" R package.\u003c/p\u003e\u003cp\u003eCluster A exhibited enrichment in both the p38 MAPK cascade and regulation of p38 MAPK cascade pathways. Previous studies have underscored the significant activation of p38 MAPK in diverse cell types within the TME[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], influencing the expression of extracellular factors such as VEGFA, IL8, and HBEGF, thereby promoting angiogenesis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, as one of the hub genes in cluster A, VWF is a polymer coagulation plasma glycoprotein and mediates platelet adherence along endothelial cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Recent research depicts its biological function including inflammation and angiogenesis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which may participate in immune cell enrichment shown in cluster A. Moreover, the somatic mutation analysis revealed a relatively high incidence of \u003cem\u003eKRAS\u003c/em\u003e mutations in Cluster A. \u003cem\u003eKRAS\u003c/em\u003e mutation is as prevalent as \u003cem\u003eEGFR\u003c/em\u003e mutation while NSCLC patients with \u003cem\u003eKRAS\u003c/em\u003e mutation responses poorly to chemotherapy and no effective inhibitors for \u003cem\u003eKRAS\u003c/em\u003e mutation for the moment[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Intriguingly, recent research suggests that \u003cem\u003eKRAS\u003c/em\u003e-mutant cases exhibit increased sensitivity to PD-1/PD-L1 inhibitors[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], implying a potential responsiveness to immunotherapy in Cluster A.\u003c/p\u003e\u003cp\u003eCluster B, on the other hand, demonstrated enrichment in IFN-γ and IFN-γ-related MHC class I pathways. Despite IFN-γ being recognized as an anti-tumor cytokine, it plays a dual role. Firstly, it serves as a marker of anti-tumor immunity. Secondly, it acts as an inducer of immune escape phenomena through multiple mechanisms. Recent studies show that IFN upregulates both classical and non-classical MHC class I genes. This process inhibits NK cells and CD8\u0026thinsp;+\u0026thinsp;T cells, leading to immune evasion, consistent with our findings in NSCLC[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. PPI analysis highlighted a significant association with \u003cem\u003eSTAT1\u003c/em\u003e in Cluster B, a key regulator in infection and inflammatory cell development[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eSTAT1\u003c/em\u003e plays a critical role in immune dysregulation, and elevated levels of \u003cem\u003eSTAT1\u003c/em\u003e expression have been reported to correlate with poorer OS in kidney cancer, low-grade glioma, lung adenocarcinoma, and pancreatic cancer[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This interpretation suggests that, from another perspective, the majority of immune cells in cluster B are in a state of immune dysregulation. In relation to the somatic mutation rate, our analysis revealed a relatively high mutation rate of \u003cem\u003eKEAP1\u003c/em\u003e in cluster B. This finding aligns with the observations of Hellyer et al., who reported that mutations in \u003cem\u003eKEAP1\u003c/em\u003e-\u003cem\u003eNFE2L2\u003c/em\u003e are significant regulators of cellular homeostasis, with mutations correlating to increased tumor growth and invasiveness\u0026mdash;a phenomenon commonly observed in NSCLC[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Elizabeth et al. reported that despite a relatively high TMB, LUAD driven by \u003cem\u003eKEAP1\u003c/em\u003e mutation shows a limited response to immunotherapy [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In our study, cluster B is distinguished by the highest TMB and the presence of \u003cem\u003eKEAP1\u003c/em\u003e mutations, traits typically associated with resistance to immunotherapy and indicative of immune evasion within this cluster. Consequently, it is imperative for us to delve deeper into understanding the interplay between the \u003cem\u003eKEAP1\u003c/em\u003e driver gene and IFN-γ, along with the IFN-γ-related MHC class I pathway. Furthermore, an exploration of the mechanisms underpinning immune evasion in cluster B is warranted.\u003c/p\u003e\u003cp\u003eIn Cluster C, the notable association with \u003cem\u003eSOX2\u003c/em\u003e has captured our attention. \u003cem\u003eSOX2\u003c/em\u003e functions as a transcription factor and plays a crucial role in various stages of embryonic development, influencing processes such as cell fate determination and differentiation[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eSOX2\u003c/em\u003e has been identified as highly expressed in LUSC, which constitutes the predominant pathological subtype in cluster C. Recent reports have indicated that overexpression and gene amplification of \u003cem\u003eSOX2\u003c/em\u003e are associated with tumor invasion and metastasis in various cancers[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Studies have demonstrated that the interaction between SOX2 and both wild-type NSD3 and the activity-enhancing mutant NSD3T1232A can induce oncogenic transformation in human tracheobronchial epithelial cells (AALE), consequently altering the TME[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This could contribute to an immune-desert phenotype and a poor prognosis in cluster C. Our findings indicate that the mutation rate of TP53 was the highest in this cluster, while the TMB was lower. Recent research has reported that Mutant p53 (Mtp53) can induce carcinogenesis through the cGAS-STING-TBK1-IRF3 pathway, suppressing both cell-autonomous and non-cell-autonomous signaling to favor cancer cell survival and escape from immune surveillance[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Cluster C exhibits an immune-desert phenotype characterized by a lower TMB, suggesting a poorer response to immunotherapy and an unfavorable prognosis.\u003c/p\u003e\u003cp\u003eWe also observed that clusters A and B are predominantly composed of LUAD, while cluster C is primarily LUSC. Cluster A exhibits the highest immune cell infiltration. Notably, the TIDE score, which has been demonstrated to more accurately predict the prognosis of patients with melanoma after anti-PD-1 or anti-CTLA4 treatment than other indicators such as PD-L1 and TMB, was lower in cluster A. Additionally, cluster A showed a higher proportion of responders to immunotherapy compared to cluster C, suggesting that LUAD may respond more favorably to immunotherapy than LUSC. This observation has potential clinical implications: LUAD patients, particularly those with Cluster A features, could be prioritized for immunotherapy trials, while LUSC patients (Cluster C) might benefit more from chemotherapy-based regimens. Such subtype-guided stratification could refine treatment decisions in NSCLC. Utilizing the GDSC database, we inferred that cluster C is more sensitive to commonly used NSCLC chemotherapy, while clusters A and B prefer small molecule targeted drugs. This finding aligns with recent studies indicating that LUSC is sensitive to chemotherapy, while LUAD responds better to targeted therapy.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eHowever, several limitations exist in our study. Firstly, our analysis relies on TCGA and GEO databases, lacking real-world immunotherapy cohorts for validating the predictions of immunotherapy efficacy. Secondly, while we constructed lncRNA molecular patterns and validated them through biostatistical analysis, the underlying mechanisms driving the discrepancies in these lncRNA molecular patterns warrant further investigation through experimental studies. Thirdly, our exploration of therapeutic sensitivity is focused on a limited number of drugs within the context of our lncRNA molecular patterns, and additional studies are needed to expand the clinical implications for a broader range of drugs. Moreover, all therapy and immunotherapy findings are computational predictions and should be interpreted cautiously until validated in experimental or clinical settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study establishes three distinct molecular patterns based on immune-related lncRNAs in NSCLC, each characterized by unique phenotypic features. We unravel potential mechanisms by which immune-related lncRNAs influence the TME. These lncRNA patterns are not only associated with the prognosis of NSCLC patients but also demonstrate predictive value for clinical responses to chemotherapy, targeted therapy, and immunotherapy. Significantly, our findings provide novel insights into immunotherapy responses and may complement conventional concepts of hot and cold tumors. These observations suggest potential implications for guiding immunotherapy or drug combination strategies, pending further validation. Furthermore, our study holds promise for identifying distinct tumor immune phenotypes, enhancing clinical responses to immunotherapy, and paving the way for tailored immunotherapeutic approaches in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization,\u0026nbsp;Ang Li and Yutao Pang; Writing,\u0026nbsp;Ang Li; Visualization,\u0026nbsp;Ang Li, Yutao Pang, Xiao Yang, Hongfei Zhang, Dong Wu, Liyao Lin, Zhan He\u0026nbsp;and\u0026nbsp;Zhu Liang\u0026nbsp;; Funding Acquisition,\u0026nbsp;Jie Chen and\u0026nbsp;Fasheng Li. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the Key Clinical Projects of Affiliated Hospital of Guangdong Medical University (LCYJ2022DL003); the supported projects of Zhanjiang (2021A05076).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available. TCGA-LUAD and TCGA-LUSC datasets were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003eGSE30219(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30219), GSE50081 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50081), GPL570 platform annotation (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL570) were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Processed data and analysis scripts are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude for the valuable data provided by TCGA and GEO. Our sincere thanks go to all individuals involved in these initiatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest associated with this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A: \u003cstrong\u003eCancer Statistics, 2021\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(1):7-33.\u003c/li\u003e\n\u003cli\u003eBrahmer J, Reckamp KL, Baas P, Crino 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e495.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, immune related lncRNA, Tumor immune microenvironment, Immune checkpoint inhibitors, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7575439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7575439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLong non-coding RNAs (lncRNAs) play critical roles in immune regulation and tumor microenvironment (TME) remodeling. However, their contribution to non-small cell lung cancer (NSCLC) heterogeneity and immunotherapy response remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe integrated transcriptomic data from TCGA and GEO cohorts after batch correction. Immune-related lncRNAs were identified and used for unsupervised clustering to define molecular subtypes. Survival outcomes, immune infiltration, somatic mutation profiles, and predicted drug sensitivities were compared among subtypes. Weighted gene co-expression network analysis (WGCNA) and pathway enrichment were performed to identify hub genes and biological processes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThree lncRNA-defined subtypes were identified with distinct TME characteristics: an immune-inflamed subtype enriched in B/T cells and HLA expression, an immune-escape subtype with interferon-driven MHC upregulation, and an immune-desert subtype with minimal immune infiltration. These subtypes were significantly associated with prognosis, genomic alterations, and clinical features. Cluster A (predominantly LUAD) exhibited superior overall survival and higher predicted immunotherapy sensitivity, while Cluster C (enriched in LUSC) showed greater responsiveness to chemotherapy. Hub genes including SOX2, KRAS, KEAP1, and STAT1 were implicated in TME regulation. Drug sensitivity prediction suggested potential therapeutic stratification across clusters.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eImmune-related lncRNA signatures define novel NSCLC subtypes with distinct immune phenotypes and therapeutic responses. These findings suggest that lncRNA-based TME classification may complement PD-L1 and tumor mutational burden as predictive biomarkers for immunotherapy. Validation in prospective clinical cohorts is warranted to establish their translational utility.\u003c/p\u003e","manuscriptTitle":"Immune-Related lncRNA Signatures Define Tumor Microenvironment Subtypes and Predict Immunotherapy Response in NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 15:14:02","doi":"10.21203/rs.3.rs-7575439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T10:37:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T04:31:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308498026193138644341424672237287742704","date":"2025-11-17T14:52:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-15T05:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208678111127331152682195005935359407688","date":"2025-11-15T04:37:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T08:11:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-15T07:56:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T18:01:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-02T10:13:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-10-01T16:40:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fa53ed1-04b4-4085-a67b-66cca26bfb6f","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T06:09:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 15:14:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7575439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7575439","identity":"rs-7575439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}