Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer

Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer | 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 Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer Huiying Ma, Yuhong Li, Tingting Wang, Yizhi Ge, Wei Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849451/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 To reveal the clinical value of cuproptosis-related genes on prognosis and metastasis in non-small cell lung cancer. Method Gene expression profiles and clinical information of non-small cell lung cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The data were grouped into training set, internal testing set, and external testing set. A risk prognostic model was constructed by Lasso-Cox regression analysis. Hub genes were identified and evaluated using immunohistochemistry and the Transwell migration assay in 50 clinical patients. Results A total of 17/19 cuproptosis-related genes were differentially expressed in tumors, 8 were significantly associated with prognosis, and 4 were markedly associated with metastasis. A risk model based on two cuproptosis-related genes was constructed and validated for predicting overall survival. The risk score was proven to be an independent risk factor for the prognosis of non-small cell lung cancer. DLAT and DLST, key genes in cuproptosis, were proven to be associated with non-small cell lung cancer prognosis and metastasis. Immunohistochemistry showed that their expression significantly predicted metastasis but failed to predict prognosis in non-small cell lung cancer patients. The transwell migration assay further increased the cellular reliability of our findings. Conclusion The cuproptosis-related genes prognostic model effectively predicted the prognosis of non-small cell lung cancer. DLAT and DLST may serve as predictive markers for metastasis in non-small cell lung cancer. Cuproptosis non-small cell lung cancer prognosis metastasis immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Lung cancer remains the deadliest malignancy in the world although much advances in immunotherapy and targeted therapies have been made. There are 28% of lung cancer patients have metastases at the time of diagnosis. Even locally advanced or early stages at diagnosis, they may still develop metastases during the disease progression. This partly cause dismal 5-year survival rate, from 60% of localized lesion to only 6% of metastases in lung cancer 1 . Non-small cell lung cancer (NSCLC) is the most common pathological type, accounting for approximately 80% lung cancer. The prognosis of NSCLC is highly correlated with the appearance of metastasis. However, most metastases in NSCLC are not diagnosed until the metastatic tumor grows to a certain size, making it difficult for clinicians to intervene in the early stages. Early identification of patients with high metastasis risk is the key to improving the survival rate in NSCLC. Therefore, new diagnostic and metastatic markers are urgently needed in order to early detect the metastases of NSCLC. Copper (Cu) is an essential micronutrient in physiology, involved in cell proliferation and death pathways. Moreover, Cu has been shown to be strongly associated with tumor progression and metastasis 2 . An imbalance of Cu can induce cell death through targeting lipoylated components of the tricarboxylic acid (TCA) cycle, which is known as cuproptosis 3 . This is a new form of regulated cell death, which is distinguished from known cell death modes such as apoptosis, pyroptosis, necroptosis, autophagy and ferroptosis. Recent studies have constructed prognostic models based on CRGs and confirmed that these models work well in the prognosis of various tumors 4 – 7 . However, studies on CRGs in NSCLC are less reported. DLAT and DLST are important lipoylated protein genes in the TCA cycle. However, there are no studies have yet to study the clinical value of DLAT and DLST in metastasis of NSCLC. Therefore, this study was designed to explore the differentially expressed CRGs in NSCLC and metastatic subgroup, and to establish a risk prognostic model. In addition, the predictive value of DLAT and DLST on the prognosis and metastasis of NSCLC was evaluated. Materials and Methods Identification of differentially expressed CRGs and prognosis-related CRGs Gene expression profiles and clinical information of patients with NSCLC were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). The limma package in R software (R version 4.1.3) was applied to normalize the gene expression data. The caret package was applied to randomly divide the NSCLC data from TCGA database into a training set and an internal testing set (1:1 ratio) for subsequent data processing. Perl (version 5.32.1) was used to collate the clinical details. A total of 19 CRGs were identified by literature reviewing. The Wilcoxon Test was used to identify CRGs differentially expressed genes in tumor and normal tissues from TCGA database ( P < 0.05, |log2 Fc|≥1), and subsequently to identify CRGs differentially expressed between the M0 and the M1 subgroup similarly. Then survival analysis was performed to identify the prognosis-related CRGs. Clinical data collection Fifty NSCLC tissues confirmed by pathology were collected from Jiangsu Cancer Hospital for validation. Twenty-five NSCLC had metastasis and 25 NSCLC had no metastasis. Immunohistochemical staining was performed on these 50 NSCLC samples, and a complete clinical follow-up of the patients was performed. The samples were sourced from our institution's biobank, and we have obtained broad informed consent from the patients. All procedures were approved by the Ethics Committee of Jiangsu Province Cancer Hospital (Grant Number: Ethics Committee of Jiangsu Cancer Hospital 2023 Department-Fast 063). Tumor clustering analysis based on CRGs The Consensus Cluster Plus package was used to divide the NSCLC from TCGA into different clusters. Overall survival (OS) rates were compared between subclusters. Then a heatmap was used to depict the clinicopathological parameters and CRGs expression of different clusters. The GSVA package was used to performing gene set enrichment analysis (GSVA) for the differential pathways. Differentially expressed genes (DEGs) between subclusters were identified (|log FC| ≥ 0.585, P < 0.05) and then analyzed for GO and KEGG enrichment. The construction, validation and evaluation of the prognostic risk model The DEGs were firstly examined by univariate Cox regression analysis to obtain prognosis-related DEGs. These prognosis-related DEGs were then analyzed by Lasso Cox regression analysis and cross-validation using glmet package in TCGA training set. Eventually a risk score prognostic model based on CRGs was constructed. Risk scores were calculated for each individual according to the prognostic model formula, and a predictive nomogram of OS was created using clinical variables and risk scores. The patients were classified into high- or low-risk groups based on the median risk score. To evaluate the predictive performance of the model, survival analysis and receiver operating characteristic (ROC) analysis were performed in the TCGA internal testing set and GEO external testing set respectively. The scores of stromal cells and immune cells were obtained based on the ESTIMATE algorithm. The ESTIMATE score was obtained by adding the scores of stromal and immune cells. Finally, the immune checkpoint genes and drug sensitivity were compared between high- and low-risk groups. Combined diagnosis and single-gene batch correlation analysis for DLAT and DLST DLAT and DLST are essential lipoylated protein genes in CRGs, and they also play an important role in the prognosis and metastasis of NSCLC. To infer whether they can predict metastasis in NSCLC, a model was constructed using binary logistic regression in SPSS. To further confirm the reliability of the result, single-gene batch correlation analysis for DLAT and DLST was performed respectively. Spearman correlation analysis was used to obtain the related genes with DLAT and DLST expression respectively, and they were listed based on the absolute correlation coefficient. Enrichment analysis was performed on the top 1000 most highly correlated genes. 1.6 IHC validation IHC was performed on 50 formalin-fixed, paraffin-embedded NSCLC tumor tissues. The IHC results were analyzed semi-quantitatively by using the histochemistry score (H-Score). The IHC technique and the H-Score calculation were as previously reported 8 . The median of the H-Score was used as the cut-off value to classify the samples into high- or low-expression groups. 1.7 Transwell migration assay NSCLC cells were transfected with DLAT-vector, DLAT sequence, si-DLAT#2, si-DLAT#3 plasmids. The migration abilities of transfected NSCLC cells were assessed using transwell chambers, following the previously described methodology 9 . After 48h of incubation, cells in the upper chamber were fixed, stained, and then counted under a microscope. 1.8 Statistics Analysis All statistical analyses were performed using R software (version 4.1.2) if not otherwise specified. The data were visualized by ggplot2. Statistical analyses were performed using SPSS (version 26) in IHC validation section. The chi-square test was used to analyze the correlation of categorical data. K-M survival analysis was used to assess the survival of NSCLC. P ≤ 0.05 was considered statistically significant. Results Differential and prognostic analysis of CRGs Gene expression profiles and clinical information of 99 normal lung tissues and 932 NSCLC tissues were obtained from the TCGA database. Differential analysis was performed based on the expression of 19 CRGs, and 17 differentially expressed CRGs were determined after comparing normal and tumor tissues. Among them, 7 CRGs (LIAS, LIPT2, DLD, DLAT, PDHA1, CDKN2A, GCSH) were up-regulated in NSCLC tissues, while 10 CRGs (NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, PDHB, MTF1, GLS, DLST) were down-regulated (Fig. 1 A). 799 of 932 NSCLC cases had complete metastasis data, and we grouped them into two subgroups (M0 for no metastasis and M1 for metastasis). 4 CRGs (LIAS, DLAT, PDHB, DLST) were over-expressed in the M1 subgroup (Fig. 1 B). In addition, 8 CRGs (DLAT, DLST, ATP7A, CDKN2A, NLRP3, SLC31A1, DLD, LIPT1) were significantly associated with prognosis ( P < 0.05). Notably, DLAT and DLST were differentially expressed in the M1 subgroup and also remarkably correlated with prognosis (Fig. 1 C). Identification of cuproptosis clusters based on CRGs To further understand the relationship between CRGs and NSCLC, two cuproptosis clusters were identified (cluster A and cluster B) (Fig. 2 A). The survival of cluster A was significantly better than cluster B (Fig. 2 B). Little difference in the clinical parameters between clusters A and B were found, but the expression of CRGs were higher in cluster A (Fig. 2 C). It can be speculated that the survival difference between clusters A and B was explained by the differential CRGs expression. This further supported that CRGs played a certain role in NSCLC. PCA analysis indicated that the two cuproptosis clusters could be distinguished, but there was partial overlap (Fig. 2 D), considering to be caused by our selection of only two principal components. The ssGSEA demonstrated that all immune cells, except CD8 + T cells and immature dendritic cells, had lower infiltration degree in cluster A than in cluster B (Fig. 2 E). The heart of the tumor immune response is CD8 + T cells, and this suggested that CRGs may influence the progression and prognosis of NSCLSC by regulating immune cell infiltration apart from CD8 + T cells. However, which and how CRGs regulate the relevant immune infiltrating cells requires further study. GSVA revealed that the two subclusters were significantly different on the functional enrichment, which may account for the prognostic difference (Fig. 2 F and Fig. 2 G). Cluster A was enriched in intraciliary transport, RNA methylation, and metabolism of fatty acids and amino acids. While cluster B was enriched in ligand-receptor interactions and collagen metabolism. Total 185 DEGs were identified in the two subclusters. They were mainly enriched on immunological functions, IL-17 signaling, ECM-receptor interactions and local adhesion pathways ( Supplementary Figure S1 ). These functions and pathways were mainly focused on immune response and tumor metastasis. Construction and validation of a prognostic model based on CRGs A risk score prognostic model was constructed by Lasso Cox regression analysis and cross-validation (Fig. 3 A and Fig. 3 B). The equation is as follows: \(risk score= \left({Exp}_{SERPINE1}\times 0.143657491357375\right)+\left({Exp}_{FAM83A}\times 0.104404411929857\right)\) . The risk scores were calculated for each individual according to the formula, and patients were divided into a high- or low-risk group based on the median risk score (Fig. 3 C and Fig. 3 D). The genes in the prognostic model were both highly expressed in the high-risk group ( Supplementary Figure S2A, B, and C ). The Sankey diagram was used to visualize differences in risk scores and in survival status among the subclusters ( Supplementary Figure S2D ). The majority of patients in cluster A were in the low-risk group, and most of them were still alive by the date of investigation. This was consistent with the fact that cluster B had a higher risk score than cluster A significantly ( Figure S2E ). To validate the capability of the model to predict prognosis, K-M survival analysis and ROC curve analysis were performed in the training set, TCGA internal testing set and GEO external testing set. Patients in TCGA internal testing set and GEO external testing set were also divided into a high- or low-risk group based on the median risk score (Fig. 4 C, Fig. 4 D, Fig. 4 G and Fig. 4 H). The survival rates of patients in the low-risk group were significantly higher than those in the high-risk group (Fig. 3 E, Fig. 4 A and Fig. 4 E). The ROC curves indicated that the model has a good predictive capability (Fig. 3 F, Fig. 4 B and Fig. 4 F). The multivariable Cox regression was visualized by nomogram, which showed that the risk score was an independent prognostic factor for NSCLC (Fig. 4 I). The calibration curves showed that the actual curves were close to the predicted curves, which confirmed the encouraging ability of the constructed model (Fig. 4 J). Analysis of tumor microenvironment and drug sensitivity based on high- and low-risk groups The tumor microenvironment (TME) is a complex environment for tumor cells to survive and progress, including immune cells, stromal cells, various signaling molecules and extracellular matrix (ECM). The stromal and ESTIMATE scores were significantly higher in the high-risk group, while the immune score was not significantly different in the two risk groups (Fig. 5 A). The tumor purity was negatively correlated with ESTIMATE scores. Therefore, we speculated that the low survival in the high-risk group was a consequence of stromal cells in TME. Previous studies have demonstrated that key components of stroma in TME promote tumor cell growth and metastasis, and also influence immunotherapy and drug sensitivity of tumors. Differential analysis of three common immune checkpoint genes showed that the expression of CD274 (known as PD-L1) was significantly higher in the high-risk group, while PDCD1 and CTLA4 were not. This illustrated that patients in the high-risk group might achieve greater clinical benefits by using immunotherapy with anti-PD-L1 immunotherapy (Fig. 5 B). It also meant that our model may provide information for screening the population for immunotherapy benefits. The half maximal inhibitory concentration (IC50) of common antitumor drugs were calculated in the two risk groups (Fig. 5 C). The smaller the IC50 was, the greater the sensitivity of the tumor to the drug. Most drugs were more sensitive in the high-risk group, including common chemotherapy drugs such as gemcitabine and paclitaxel-like drugs, as well as common targeted drugs, like tyrosine kinase inhibitors (TKIs). It can be seen that NSCLC in the high-risk group was more sensitive to chemotherapy and targeted therapy. Combined diagnosis and single-gene batch correlation analysis for DLAT and DLST Protein lipoylated was a crucial process in cuproptosis, and DLAT and DLST were proven to be essential lipoylated protein genes. In addition, they were differentially expressed both in NSCLC and in the M1 subgroup, and high expression of them was associated with poorer prognosis (Fig. 6 ). We thus wondered whether the combined diagnosis of DLAT and DLST could predict metastasis in NSCLC. A model was constructed to calculate the probability of metastasis in NSCLC. The AUCs of DLAT and DLST was 0.555 and 0.446 respectively, while the AUC increased to 0.578 after combining the DLAT and DLST. This proved that the combined diagnosis of DLAT and DLST can predict the metastasis in NSCLC to some extent. However, it is unclear how they affect metastasis and prognosis in NSCLC because the functions of them are poorly studied. Genes in the top 1000 correlated with DLAT and DLST were obtained respectively, and GO enrichment analysis was performed on them. DLAT-correlated genes were involved in ATP hydrolysis, GTPase binding, ribonucleoproteins (RNPs) biogenesis, and RNA splicing. DLST-correlated genes were involved in ATP hydrolysis, RNA helicase activity, RNPs biogenesis and RNA splicing, ( Figure S3 ). Their involvement in ATP metabolic was consistent with the fact that tumor cells require high energy for metastasis. In addition, they were involved in RNPs biogenesis and RNA splicing, which could also affect tumor cell metastasis. IHC validation for DLAT and DLST Immunohistochemical validation was performed on 50 NSCLC samples, 4 cases are shown in Fig. 7 . The samples were divided into high- or low-expression group based on the median H-Score of DLAT and DLST. The clinicopathological characteristics and their correlation with the expression of DLAT and DLST are shown in Table 1 . DLAT expression was significantly correlated with the M stage, but it was not correlated with gender, age, T and N stages, pathology, and brain metastasis status. DLST expression was significantly correlated with T, N, and M stages, pathology, and brain metastasis status, but not age and gender. DLAT and DLST expression were both significantly associated with metastasis, and univariate and multifactorial logistic regression analyses showed that gender, expression of DLAT and DLST were all independent predictive factors for metastasis (Table 2 ). Males in gender, high DLAT-expressing, and low DLST-expressing of NSCLC were more likely to have metastasis. This aligns precisely with our bioinformatics analysis results, indicating a significantly increased expression of DLAT within the M1 subgroup. However, the IHC suggested that DLST expression was lower in the M1 subgroup, which probably resulted from limitations in sample source. That is, DLAT and DLST expression can predict metastasis in NSCLC. Table 1 Clinicopathological features of 50 NSCLC patients Characteristics Number DLAT DLST Low High P value Low High P value Gender Male 33 18 15 0.551 16 17 1 Female 17 7 10 9 8 Age/ Years <60 years 18 8 10 0.769 7 11 0.377 ≥ 60 years 32 17 15 18 14 Pathology Squamous carcinoma 5 3 2 1 0 5 0.05 Adenocarcinoma 45 22 23 25 20 T T1-2 37 18 19 1 23 14 0.008 T3-4 12 6 6 2 10 Tx 1 1 0 0 1 N N0 10 3 7 0.306 9 1 0.01 N1-3 36 18 18 14 22 Nx 4 4 0 2 2 M M0 25 8 17 0.023 18 7 0.004 M1 25 17 8 7 18 Brain Metastasis Yes 5 3 2 1 0 5 0.05 No 45 22 23 25 20 PD-L1 <1% 20 7 13 0.039 11 9 0.831 1 ~ 49% 16 7 9 8 8 ≥ 50% 14 11 3 6 8 Table 2 Univariate and multifactorial logistic analysis of metastasis Univariate analysis Multivariate analysis Items HR 95%CI P value HR 95%CI P value Gender 3.692 1.052–12.957 0.041 4.802 1.015–22.718 0.048 DLAT 4.516 1.376–14.820 0.013 4.037 1.022–15.954 0.047 DLST 0.151 0.044–0.520 0.003 0.135 0.032–0.559 0.006 In addition, the expression of DLST showed a correlation with brain metastasis status ( P = 0.05), so DLST can help clinicians identify high-risk brain metastasis patients and thus prevent brain metastasis in advance. The middle PFS was 14 and 25 months ( P = 0.134) for the DLAT high and low-expression groups, and 22 and 24 months ( P = 0.226) for the DLST high and low-expression groups. This means that both DLAT and DLST were not significantly different in survival. Among the 24 NSCLC patients with immunotherapy, the middle PFS for DLAT high and low expression groups were 7 and 24 months ( P = 0.018), and the middle PFS for DLST high and low expression groups were 15 and 12 months ( P = 0.552). This suggested that DLAT might predict NSCLC immunotherapy efficacy, consistent with a fact that DLAT expression was significantly correlated with PD-L1 expression ( P = 0.039). DLAT expression affects cell migration in NSCLC cells From the transwell assay, the number of migration cells were significantly increased in A549 and HCC827 cells transfected with oeDLAT compared to cells transfected with the vector control (Fig. 8 A and Fig. 8 C). Subsequently, we knocked down DLAT in A549 and HCC827 cells, and assessed the migrated cell number in these cell lines. As depicted in Fig. 8 B and Fig. 8 D, the knockdown of DLAT led to a reduction in cell migration in both A549 and HCC827 cells compared to the vector control-transfected cells. Notably, the knockdown of DLAT remarkably impaired the capabilities of migration in A549 and HCC827 cells. An illustrative case of DLAT for predicting immunotherapy efficacy in NSCLC To demonstrate visually the therapeutic response of NSCLC lesions to anti-PD-L1-immunotherapy, we examed an illustrative case who showed a low DLAT expression. A 61-year-old NSCLC patient received anti-PD-L1-immunotherapy. CT showed the lung tumor size of 8.31×6.17 cm before the patient received immunotherapy. 9 months after treatment, CT revealed the tumor size decreased to 4.70×3.93 cm (Fig. 9 ). The patient's condition was assessed for PR according to the iRECIST evaluation criteria. This further demonstrates that low DLAT expression predicts good immunotherapy efficacy. Discussion Previous studies have revealed that Cu is closely involved in cell proliferation, tumor metastasis, angiogenesis and the remodeling of tumor microenvironments via various molecular mechanisms 10 , 11 . Moreover, Cu can regulate the expression of PD-L1 in tumor cells, thus affecting anti-PD-L1 antitumor therapy 12 . The above studies all illustrate the importance of Cu in tumor cells. Recently, it was found that excess intracellular copper can trigger a new form of cell death by targeting lipoylated proteins in the TCA cycle. This means that Cu combines with lipoylated proteins, causing aggregation of lipoylated protein and loss of iron-sulfur cluster protein. It then leads to proteotoxic stress and ultimately cell death 3 . This particular type of cell death is known as cuproptosis and is receiving increasing attention. However, the prognostic value of CRGs in NSCLC is unknow. DLAT and DLST are essential lipoylated protein genes in the TCA cycle. As shown in the part of our bioinformatics analysis, DLAT and DLST played an essential role both in the metastasis and prognosis of NSCLC. However, the result is only based on public databases and still need to be supported by experiments and clinical data. Therefore, our study not only explored the prognostic value of the CRGs-related prognostic model but innovatively analyzed the value of DLAT and DLST in metastasis for NSCLC. In our study, we identified two cuproptosis clusters based on CRGs, where patients in cluster A had a significantly better prognosis than cluster B. CRGs in NSCLC are upregulated in the majority of cluster A, with LIPT1 most notably. It has been reported that LIPT1 is upregulated in melanoma and is an independent favorable prognostic indicator 13 .Therefore, LIPT1 may play a similar role in inhibiting tumor progression in NSCLC. In addition, 185 DEGs were identified in these two clusters. The above results initially confirm the potential value of CRGs in NSCLC. Our risk score prognostic model ultimately identified two genes, SERPINE1 and FAM83A. SERPINE1, an inhibitor of plasminogen activator, was expected to protect against tumor proliferation by inhibiting plasminogen activator activity. However, several studies have confirmed that SERPINE1 was significantly upregulated in a variety of malignancies, including glioblastoma, esophageal squamous carcinoma, breast cancer, gastric cancer, bladder cancer, and oral squamous carcinoma. It is also an important marker of poor prognosis in various malignancies 14 – 20 . Therefore, Miguel et al. 21 concluded that SERPINE1 was responsible for the poor prognosis by multiple signaling pathways which were independent of the plasminogen activator (PA) system. FAM83A, a family member A gene with sequence similarity 83, was identified as a tumor-promoting gene that is upregulated in several malignancies and was significantly associated with poor prognosis in tumors 22 – 26 . In addition, overexpression of FAM83A may affect PD-L1 expression and resistance to EGFR-TKI drugs, which may affect the efficacy of immunotherapy and targeted therapy 27 , 28 . It was evident that the two genes included in the risk model were closely related to the progression and prognosis of tumors. For this reason, it is well-supported to use them to construct the prognostic models. NSCLC is a highly heterogeneous tumor with relatively complex treatment and highly different survival rates. The treatment and prognosis of NSCLC differ markedly by individual, even if identical in the stage. In clinical practice, molecular pathology test results of NSCLC are used to predict the development of NSCLC and help clinicians to individualize treatment, but they are not completely accurate. We classified NSCLC into high- and low-risk groups based on the risk prognostic score model. The survival of the low-risk group is remarkably better than the high-risk group. Moreover, risk score was an independent risk factor for NSCLC prognosis. All of these supported the potential clinical value of the risk score. In tumor tissues, the purity of tumor cells was lower in the high-risk group for NSCLC, while the immune infiltration did not differ significantly compared with the low-risk group. It suggested that the poorer prognosis in the high-risk group may be explained by the stromal component in the tumor tissues. Some components of the stroma, such as cytokines produced by cancer-associated fibroblasts (CAF) and tumor-associated macrophages (TAM), promote infiltration and metastasis of tumor cells 29 . In general, an increase in stromal components, such as extracellular matrix (ECM), may increase drug resistance in tumor cells. However, our study showed that the high-risk group was more sensitive to some common antitumor drugs. It indicates that CRGs may affect the genetics or epigenetics of tumor cells rather than TME, resulting in higher drug sensitivity in the high-risk group. PD-L1 expression was significantly higher in the high-risk group, suggesting that the high-risk group may be more sensitive to immunotherapy. In summary, the model we constructed could be a valuable tool for risk classification of NSCLC. It has a good ability to predict the prognosis of NSCLC and to guide clinical individualized treatment. Currently, most of the metastases in NSCLC are clinically diagnosed through imaging. However, imaging is difficult to confirm early metastases and there is also a certain rate of false positives and false negatives. This leads to the clinicians' failure to detect the sign of metastasis in time, so there is a demand for higher diagnostic efficacy of metastasis in clinical practice. Immunotherapy based on immune checkpoint inhibitors led to a leap forward in the treatment of NSCLC, increasing the 5-year survival rate of NSCLC to 16% for the first time, but only some of the patients benefit from immunotherapy. PD-L1 testing has been included in the treatment guideline for NSCLC for screening potential beneficiaries of immunotherapy. However, PD-L1 testing is still an effective but imperfect tool and not an absolute marker for immunotherapy due to various limitations 30 . In this study, we used bioinformatics to discover that DLAT and DLST play an important role in metastasis in NSCLC. However, there are no studies on their role in tumor metastasis. Single-gene bulk correlation analysis indicated that the functions of them have a large partial overlap and were mainly focused on the processes of RNA splicing and processing and synthesis of RNPs. It has been reported that dysregulation of the RNA splicing was associated with increased invasion, angiogenesis, metastasis, and drug resistance of cancer cells 31 . In addition, RNPs may also influence tumor progression through diverse mechanisms. Interestingly, the spliceosome is one of the members of RNPs. Moreover, growing evidence strongly identifies an essential link between the EMT program and Ribonucleoprotein (RNP) biogenesis 32 , 33 , which leads to tumor cell migration, invasion, and eventual metastasis. Previous studies shows that high expression of DLAT is associated with metastasis in liver and colon cancer, predicting a poor prognosis 34 , 35 . Depletion and inhibition of DLST decrease invasion and metastasis of triple-negative breast cancer (TNBC) cells 36 . In conclusion, these studies provide a theoretical basis for our study that explores the value of DLAT and DLST in NSCLC metastasis. The results of IHC analysis were generally consistent with the results of the bioinformatic analysis. DLAT and DLST expressions were significantly correlated with metastasis and have the potential to be markers for predicting metastasis in NSCLC. Moreover, both DLAT and DLST expression were independent predictive factors for metastasis in NSCLC. In the Transwell migration assay, we observed that the overexpression of DLAT significantly increased the migratory capacity of cells in vitro, while the knockdown of DLAT effectively inhibited cell migration. These results contribute to a better understanding of the molecular mechanisms underlying the metastatic process in NSCLC and may have implications for the development of targeted therapies. However, these results are limited to in vitro experiments. To further explore the molecular mechanisms of DLAT, additional in vivo experiments and molecular pathway studies are needed. NSCLC patients have a high rate of brain metastasis, which response poorly to conventional drug therapy due to the presence of the blood-brain barrier. Fortunately, we found that DLST expression has the potential to predict brain metastasis, so clinicians may expect to selectively take measures to prevent brain metastasis based on DLST expression, thus making treatment more individualized. This may enable more patients to benefit from brain radiotherapy. Furthermore, although our study demonstrated that DLAT and DLST were strongly associated with metastasis, the IHC results showed that none of their associations were reflected in the prognosis. In our study, DLAT expression significantly correlated with PD-L1, and low DLAT expression indicated better immunotherapeutic efficacy, while DLST did not. It has been confirmed that CRGs could predict immunotherapy efficacy for breast cancer 37 . It may provide preliminary evidence for the potential of DLAT in predicting the efficacy of anti-PD-L1 immunotherapy for NSCLC. Therefore, we need more in-depth studies to explore the value of DLAT and DLST in tumor cells and to determine their accuracy and reliability as potential markers. In conclusion, the prognostic model constructed based on CRGs has a good predictive ability for the prognosis of NSCLC, and may provide a reference for the clinical interventions. DLAT may play a role in predicting immunotherapy efficacy. In addition, DLAT and DLST are expected to serve as markers of metastasis in NSCLC. Declarations Funding This work was supported by funding from the National Natural Science Foundation of China (Grant Number 82002869). Competing Interest The authors declare that the research was conducted in the absence of any financial or non-financial interests that could be construed as a potential conflict of interest. Data Availability Statements The information of TCGA and GEO databases can be found at the following website: https://portal.gdc.cancer.gov/, https://www.ncbi.nlm.nih.gov/geo/. The data that support the findings of this study are available in this article and its supplementary files. Further inquiries can be directed to the corresponding authors. Acknowledgments The authors would like to thank the reviewers for their helpful comments on this article, as well as the TCGA and GEO databases for kindly providing the data. Author contributions YG and WC designed the study and contributed to the experiments of this study. HM and YL performed the statistical analysis and wrote the manuscript. TW provided technical guidance. YG and WC participated in the manuscript revision. All authors contributed to the article and reviewed the manuscript. Ethics approval All procedures were approved by the Ethics Committee of Jiangsu Province Cancer Hospital. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Jiangsu Province Cancer Hospital (Grant Number江苏省肿瘤医院伦理委员会2023 科-快 063). Consent for publication Not applicable. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33. Xie J, Yang Y, Gao Y, He J. Cuproptosis: mechanisms and links with cancers. Mol Cancer. 2023;22(1):46. Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375(6586):1254-1261. Wang W, Lu Z, Wang M, et al. The cuproptosis-related signature associated with the tumor environment and prognosis of patients with glioma. Front Immunol. 2022;13:998236. Sha S, Si L, Wu X, et al. 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Defining the human copper proteome and analysis of its expression variation in cancers. Metallomics. 2017;9(2):112-123. Voli F, Valli E, Lerra L, et al. Intratumoral Copper Modulates PD-L1 Expression and Influences Tumor Immune Evasion. Cancer Res. 2020;80(19):4129-4144. Lv H, Liu X, Zeng X, et al. Comprehensive Analysis of Cuproptosis-Related Genes in Immune Infiltration and Prognosis in Melanoma. Front Pharmacol. 2022;13:930041. Zhao C, Liu Z. MicroRNA 617 Targeting SERPINE1 Inhibited the Progression of Oral Squamous Cell Carcinoma. Mol Cell Biol. 2021;41(6):e0056520. Chen S, Li Y, Zhu Y, et al. SERPINE1 Overexpression Promotes Malignant Progression and Poor Prognosis of Gastric Cancer. J Oncol. 2022;2022:2647825. Zhang W, Xu J, Fang H, et al. Endothelial cells promote triple-negative breast cancer cell metastasis via PAI-1 and CCL5 signaling. Faseb j. 2018;32(1):276-288. Xu J, Zhang W, Tang L, Chen W, Guan X. Epithelial-mesenchymal transition induced PAI-1 is associated with prognosis of triple-negative breast cancer patients. Gene. 2018;670:7-14. Vachher M, Arora K, Burman A, Kumar B. NAMPT, GRN, and SERPINE1 signature as predictor of disease progression and survival in gliomas. J Cell Biochem. 2020;121(4):3010-3023. Sakamoto H, Koma YI, Higashino N, et al. PAI-1 derived from cancer-associated fibroblasts in esophageal squamous cell carcinoma promotes the invasion of cancer cells and the migration of macrophages. Lab Invest. 2021;101(3):353-368. Li X, Dong P, Wei W, et al. Overexpression of CEP72 Promotes Bladder Urothelial Carcinoma Cell Aggressiveness via Epigenetic CREB-Mediated Induction of SERPINE1. Am J Pathol. 2019;189(6):1284-1297. Pavón MA, Arroyo-Solera I, Céspedes MV, Casanova I, León X, Mangues R. uPA/uPAR and SERPINE1 in head and neck cancer: role in tumor resistance, metastasis, prognosis and therapy. Oncotarget. 2016;7(35):57351-57366. Zhao J, Zhao F, Yang T, et al. FAM83A has a pro-tumor function in ovarian cancer by affecting the Akt/Wnt/β-catenin pathway. Environ Toxicol. 2022;37(4):695-707. Ji H, Song H, Wang Z, et al. FAM83A promotes proliferation and metastasis via Wnt/β-catenin signaling in head neck squamous cell carcinoma. J Transl Med. 2021;19(1):423. Liu PJ, Chen YH, Tsai KW, et al. Involvement of MicroRNA-1-FAM83A Axis Dysfunction in the Growth and Motility of Lung Cancer Cells. Int J Mol Sci. 2020;21(22). Zhou F, Geng J, Xu S, et al. FAM83A signaling induces epithelial-mesenchymal transition by the PI3K/AKT/Snail pathway in NSCLC. Aging (Albany NY). 2019;11(16):6069-6088. Jin Y, Yu J, Jiang Y, et al. Comprehensive analysis of the expression, prognostic significance, and function of FAM83 family members in breast cancer. World J Surg Oncol. 2022;20(1):172. Zhou F, Wang X, Liu F, Meng Q, Yu Y. FAM83A drives PD-L1 expression via ERK signaling and FAM83A/PD-L1 co-expression correlates with poor prognosis in lung adenocarcinoma. Int J Clin Oncol. 2020;25(9):1612-1623. Lee SY, Meier R, Furuta S, et al. FAM83A confers EGFR-TKI resistance in breast cancer cells and in mice. J Clin Invest. 2012;122(9):3211-3220. Turley SJ, Cremasco V, Astarita JL. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015;15(11):669-682. Doroshow DB, Bhalla S, Beasley MB, et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18(6):345-362. Bradley RK, Anczuków O. RNA splicing dysregulation and the hallmarks of cancer. Nat Rev Cancer. 2023;23(3):135-155. Prakash V, Carson BB, Feenstra JM, et al. Ribosome biogenesis during cell cycle arrest fuels EMT in development and disease. Nat Commun. 2019;10(1):2110. Dass RA, Sarshad AA, Carson BB, et al. Wnt5a Signals through DVL1 to Repress Ribosomal DNA Transcription by RNA Polymerase I. PLoS Genet. 2016;12(8):e1006217. Wang T, Li C-x, Nan P, et al. Analysis of metabolic associated gene DLAT expression in colorectal cancer based on multidatabase and its clinical significance. Chen X, Sun M, Feng W, et al. An integrative analysis revealing cuproptosis-related lncRNAs signature as a novel prognostic biomarker in hepatocellular carcinoma. Front Genet. 2023;14:1056000. Shen N, Korm S, Karantanos T, et al. DLST-dependence dictates metabolic heterogeneity in TCA-cycle usage among triple-negative breast cancer. Commun Biol. 2021;4(1):1289. Song S, Zhang M, Xie P, Wang S, Wang Y. Comprehensive analysis of cuproptosis-related genes and tumor microenvironment infiltration characterization in breast cancer. Front Immunol. 2022;13:978909. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx 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-3849451","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268422773,"identity":"983fa989-68e5-4cc6-9cbc-6bd0a24d24a6","order_by":0,"name":"Huiying Ma","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University \u0026 Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Huiying","middleName":"","lastName":"Ma","suffix":""},{"id":268422774,"identity":"172a2815-5864-4057-a9f2-333e7d0bfe7d","order_by":1,"name":"Yuhong Li","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University \u0026 Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Li","suffix":""},{"id":268422775,"identity":"61417cdb-1409-44b1-ad9a-7e18423273be","order_by":2,"name":"Tingting Wang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University \u0026 Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Wang","suffix":""},{"id":268422776,"identity":"bc8b31d3-aaa1-4f9f-a793-4058996bf5c0","order_by":3,"name":"Yizhi Ge","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University \u0026 Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Yizhi","middleName":"","lastName":"Ge","suffix":""},{"id":268422777,"identity":"df8fa264-c730-4602-9b4d-3a8dd389e0bb","order_by":4,"name":"Wei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYFACxgZmCIP5wIEPP0jTwpZ4cGYPkfZAtfAYH+ZgI0K5wfHmxs8FFXfydGfkfDjMwMMgzy92gICWMwebpWeceVZsdiN3w+ECCwbDmbMT8Gsxu5HYxszbdjhxG0jLDB6GBIPbhLTcfwjU8g+kJefBYR42YrTcYARqaQBrYSBOi/2ZxGZpnmNALWeeGQADWYKwXyTbjz/8zFMD1HI8+fGHDz9s5PmlCWhBBxKkKR8Fo2AUjIJRgB0AACoCTMw74aGrAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Cancer Hospital of Nanjing Medical University \u0026 Jiangsu Cancer Hospital \u0026 Jiangsu Institute of Cancer Research","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-01-10 04:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849451/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849451/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49977029,"identity":"0fc81c2f-2067-4d57-95a0-256e12f0b1fd","added_by":"auto","created_at":"2024-01-22 14:52:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189311,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis and prognostic analysis of CRGs. \u003cstrong\u003eA.\u003c/strong\u003e Differential analysis of CRGs in normal and NSCLC tissues (blue: normal tissues; red: NSCLC tissues).\u003cstrong\u003e B.\u003c/strong\u003e Differential analysis of CRGs between M0 and M1 (blue: M0; red: M1). \u003cstrong\u003eC.\u003c/strong\u003e CRGs significantly associated with prognosis (blue: low expression; red: high expression). *\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":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/a0f6691eb86f437add13bdcd.png"},{"id":49978349,"identity":"a0a24896-0cf2-4b57-a261-6c88f5f3cad7","added_by":"auto","created_at":"2024-01-22 15:00:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555175,"visible":true,"origin":"","legend":"\u003cp\u003eTumor consensus clustering analysis based on CRGs. \u003cstrong\u003eA. \u003c/strong\u003eNSCLC samples were classified into two clusters utilizing the consensus clustering matrix (k = 2). \u003cstrong\u003eB. \u003c/strong\u003eComparing OS rates via KM survival analysis for the two clusters. \u003cstrong\u003eC. \u003c/strong\u003eThe heatmap depicting the clinicopathological parameters and CRGs expression of the two clusters. \u003cstrong\u003eD.\u003c/strong\u003e PCA plot for NSCLC patients depending on clusters. \u003cstrong\u003eE.\u003c/strong\u003e GO function analysis in the two clusters. \u003cstrong\u003eF.\u003c/strong\u003e KEGG pathway analysis in the two clusters.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/ab92a63ce8af08db551078f5.png"},{"id":49978348,"identity":"2d130ea9-c04f-4b39-a1b0-28a2626f87e4","added_by":"auto","created_at":"2024-01-22 15:00:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":235652,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of risk signature in the train cohort. \u003cstrong\u003eA.\u003c/strong\u003e LASSO regression analysis of DEGs. \u003cstrong\u003eB.\u003c/strong\u003e Cross-validation results of Lasso Cox regression analysis. \u003cstrong\u003eC.\u003c/strong\u003e Distributions of OS status, OS, and risk scores. \u003cstrong\u003eD.\u003c/strong\u003e Distribution of patients based on the risk score (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). \u003cstrong\u003eE.\u003c/strong\u003eKaplan–Meier curves of patients in the high-risk and low-risk groups.\u003cstrong\u003e F. \u003c/strong\u003eROC curves demonstrated the predictive efficiency of the risk score.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/c91c349686b90de818789f80.png"},{"id":49977032,"identity":"910cea58-1ad7-4f9d-b9d4-b32192fa66fd","added_by":"auto","created_at":"2024-01-22 14:52:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273235,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the risk model. \u003cstrong\u003eA.\u003c/strong\u003e Kaplan–Meier curves between low-risk and high-risk groups in TCGA internal testing set. \u003cstrong\u003eB.\u003c/strong\u003e ROC curves for the prognostic model\u003cstrong\u003e \u003c/strong\u003ein TCGA internal testing set.\u003cstrong\u003eC.\u003c/strong\u003e Distribution of patients in internal testing set. \u003cstrong\u003eD.\u003c/strong\u003e Distributions of OS status, OS, and risk scores in TCGA internal testing set. \u003cstrong\u003eE.\u003c/strong\u003eKaplan–Meier curves between low-risk and high-risk groups in GEO external testing set. \u003cstrong\u003eF.\u003c/strong\u003e ROC curves for the prognostic model\u003cstrong\u003e \u003c/strong\u003ein GEO external testing set\u003cstrong\u003e. G. \u003c/strong\u003eDistribution of patients in the GEO external testing set based on risk scores. \u003cstrong\u003eH.\u003c/strong\u003e Distributions of OS status, OS, and risk scores in GEO external testing set. \u003cstrong\u003eI. \u003c/strong\u003eThe total score of the patient in the nomogram was 150, which indicated that the survival probability of this patient at 1-, 3-, and 5-years was 87.1, 64.3, and 47.5%, respectively. \u003cstrong\u003eJ \u003c/strong\u003eCalibration plots for agreement tests between predicted and actual OS.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/a23581a182b5b093a621b3c3.png"},{"id":49978734,"identity":"46e18661-4a19-4a9c-af69-be03f1ec7bd5","added_by":"auto","created_at":"2024-01-22 15:08:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":363914,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of tumor microenvironment and drug sensitivity based on high- and low-risk groups. \u003cstrong\u003eA.\u003c/strong\u003e Differential analysis of immune microenvironment scores between high- and low-risk groups. \u003cstrong\u003eB. \u003c/strong\u003eDifferential analysis of gene expression of three immune checkpoints between high and low risk groups. \u003cstrong\u003eC.\u003c/strong\u003eSome drugs with different sensitivities in high- and low-risk groups. *\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":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/bc4db1e1d7e524e7036314e4.png"},{"id":49978735,"identity":"8b077bd1-e5ef-4588-b8c8-e25dfa14019a","added_by":"auto","created_at":"2024-01-22 15:08:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":157586,"visible":true,"origin":"","legend":"\u003cp\u003eDLAT and DLST were identified hubgenes in NSCLC.\u003cstrong\u003e A.\u003c/strong\u003e DLAT and DLST were differentially expressed in NSCLC tissues. \u003cstrong\u003eB. \u003c/strong\u003eDLAT and DLST were differentially expressed in M1 subgroup. \u003cstrong\u003eC.\u003c/strong\u003e High expression of DLAT was significantly associated with poorer prognosis. \u003cstrong\u003eD.\u003c/strong\u003e High expression of DLST was significantly associated with poorer prognosis. *\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":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/ff37158740dabea50fdfd5a6.png"},{"id":49977037,"identity":"4a918341-d244-4e38-9f2a-051fb1819901","added_by":"auto","created_at":"2024-01-22 14:52:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3281279,"visible":true,"origin":"","legend":"\u003cp\u003eThe immunohistochemical staining of DLAT and DLST in NSCLC tissues. \u003cstrong\u003eA. \u003c/strong\u003e\u0026nbsp;DLAT immunohistochemical staining of NSCLC tissues from 4 different patients. \u003cstrong\u003eB.\u003c/strong\u003e DLST immunohistochemical staining of NSCLC tissues from the above 4 patients.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/e11f6fe0c014894a37b0faed.png"},{"id":49977031,"identity":"c874e9cd-8a09-4106-9a4b-fb3b318462ab","added_by":"auto","created_at":"2024-01-22 14:52:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2450367,"visible":true,"origin":"","legend":"\u003cp\u003eDLAT modulates migration in NSCLC cells. \u003cstrong\u003eA.\u003c/strong\u003e Migratory ability of A549 cells after DLAT overexpression. \u003cstrong\u003eB.\u003c/strong\u003e Migratory ability of A549 cells after DLAT knockdown. \u003cstrong\u003eC.\u003c/strong\u003e Migratory ability of HCC827 cells after DLAT overexpression. \u003cstrong\u003eD.\u003c/strong\u003e Migratory ability of HCC827 cells after DLAT knockdown.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/0f92496737876b3731466d18.png"},{"id":49977038,"identity":"69424bd7-9cb8-4560-9fed-3e9ba8eb00ee","added_by":"auto","created_at":"2024-01-22 14:52:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":440910,"visible":true,"origin":"","legend":"\u003cp\u003eAn illustrative case of a 61-year-old NSCLC man with anti-PD-L1 immunotherapy on March 2, 2021. \u003cstrong\u003eA. \u003c/strong\u003eChest enhancement CT before treatment on January 26, 2021, the tumor size was 8.31×6.17 cm. \u003cstrong\u003eB.\u003c/strong\u003e Chest enhancement CT after 4 months of immunotherapy on May 26, 2021, tumor size was 5.61×4.53 cm. \u003cstrong\u003eC.\u003c/strong\u003e Chest enhancement CT after 9 months of immunotherapy on October 20, 2021, tumor size was 4.70×3.93 cm.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/b7c36c49a3e8d81b43e69f0c.png"},{"id":51731963,"identity":"fc4743f7-2fb2-4b5d-911d-a3ac9c5ad32c","added_by":"auto","created_at":"2024-02-28 05:14:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3621293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/04a77d9d-b012-4c23-a614-eceaff3c3912.pdf"},{"id":49977033,"identity":"35742b96-c622-49c2-924b-5b8707bbfe22","added_by":"auto","created_at":"2024-01-22 14:52:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1089205,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3849451/v1/38e675b965f05acbc860b59d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the deadliest malignancy in the world although much advances in immunotherapy and targeted therapies have been made. There are 28% of lung cancer patients have metastases at the time of diagnosis. Even locally advanced or early stages at diagnosis, they may still develop metastases during the disease progression. This partly cause dismal 5-year survival rate, from 60% of localized lesion to only 6% of metastases in lung cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Non-small cell lung cancer (NSCLC) is the most common pathological type, accounting for approximately 80% lung cancer. The prognosis of NSCLC is highly correlated with the appearance of metastasis. However, most metastases in NSCLC are not diagnosed until the metastatic tumor grows to a certain size, making it difficult for clinicians to intervene in the early stages. Early identification of patients with high metastasis risk is the key to improving the survival rate in NSCLC. Therefore, new diagnostic and metastatic markers are urgently needed in order to early detect the metastases of NSCLC.\u003c/p\u003e \u003cp\u003eCopper (Cu) is an essential micronutrient in physiology, involved in cell proliferation and death pathways. Moreover, Cu has been shown to be strongly associated with tumor progression and metastasis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. An imbalance of Cu can induce cell death through targeting lipoylated components of the tricarboxylic acid (TCA) cycle, which is known as cuproptosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This is a new form of regulated cell death, which is distinguished from known cell death modes such as apoptosis, pyroptosis, necroptosis, autophagy and ferroptosis. Recent studies have constructed prognostic models based on CRGs and confirmed that these models work well in the prognosis of various tumors\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, studies on CRGs in NSCLC are less reported. DLAT and DLST are important lipoylated protein genes in the TCA cycle. However, there are no studies have yet to study the clinical value of DLAT and DLST in metastasis of NSCLC.\u003c/p\u003e \u003cp\u003eTherefore, this study was designed to explore the differentially expressed CRGs in NSCLC and metastatic subgroup, and to establish a risk prognostic model. In addition, the predictive value of DLAT and DLST on the prognosis and metastasis of NSCLC was evaluated.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed CRGs and prognosis-related CRGs\u003c/h2\u003e \u003cp\u003eGene expression profiles and clinical information of patients with NSCLC were downloaded from 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) and Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The limma package in R software (R version 4.1.3) was applied to normalize the gene expression data. The caret package was applied to randomly divide the NSCLC data from TCGA database into a training set and an internal testing set (1:1 ratio) for subsequent data processing. Perl (version 5.32.1) was used to collate the clinical details. A total of 19 CRGs were identified by literature reviewing. The Wilcoxon Test was used to identify CRGs differentially expressed genes in tumor and normal tissues from TCGA database (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2 Fc|\u0026ge;1), and subsequently to identify CRGs differentially expressed between the M0 and the M1 subgroup similarly. Then survival analysis was performed to identify the prognosis-related CRGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical data collection\u003c/h2\u003e \u003cp\u003eFifty NSCLC tissues confirmed by pathology were collected from Jiangsu Cancer Hospital for validation. Twenty-five NSCLC had metastasis and 25 NSCLC had no metastasis. Immunohistochemical staining was performed on these 50 NSCLC samples, and a complete clinical follow-up of the patients was performed. The samples were sourced from our institution's biobank, and we have obtained broad informed consent from the patients. All procedures were approved by the Ethics Committee of Jiangsu Province Cancer Hospital (Grant Number: Ethics Committee of Jiangsu Cancer Hospital 2023 Department-Fast 063).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTumor clustering analysis based on CRGs\u003c/h2\u003e \u003cp\u003eThe Consensus Cluster Plus package was used to divide the NSCLC from TCGA into different clusters. Overall survival (OS) rates were compared between subclusters. Then a heatmap was used to depict the clinicopathological parameters and CRGs expression of different clusters. The GSVA package was used to performing gene set enrichment analysis (GSVA) for the differential pathways. Differentially expressed genes (DEGs) between subclusters were identified (|log FC| \u0026ge; 0.585, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and then analyzed for GO and KEGG enrichment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eThe construction, validation and evaluation of the prognostic risk model\u003c/h2\u003e \u003cp\u003eThe DEGs were firstly examined by univariate Cox regression analysis to obtain prognosis-related DEGs. These prognosis-related DEGs were then analyzed by Lasso Cox regression analysis and cross-validation using glmet package in TCGA training set. Eventually a risk score prognostic model based on CRGs was constructed. Risk scores were calculated for each individual according to the prognostic model formula, and a predictive nomogram of OS was created using clinical variables and risk scores. The patients were classified into high- or low-risk groups based on the median risk score. To evaluate the predictive performance of the model, survival analysis and receiver operating characteristic (ROC) analysis were performed in the TCGA internal testing set and GEO external testing set respectively. The scores of stromal cells and immune cells were obtained based on the ESTIMATE algorithm. The ESTIMATE score was obtained by adding the scores of stromal and immune cells. Finally, the immune checkpoint genes and drug sensitivity were compared between high- and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCombined diagnosis and single-gene batch correlation analysis for DLAT and DLST\u003c/h2\u003e \u003cp\u003eDLAT and DLST are essential lipoylated protein genes in CRGs, and they also play an important role in the prognosis and metastasis of NSCLC. To infer whether they can predict metastasis in NSCLC, a model was constructed using binary logistic regression in SPSS. To further confirm the reliability of the result, single-gene batch correlation analysis for DLAT and DLST was performed respectively. Spearman correlation analysis was used to obtain the related genes with DLAT and DLST expression respectively, and they were listed based on the absolute correlation coefficient. Enrichment analysis was performed on the top 1000 most highly correlated genes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.6 IHC validation\u003c/h2\u003e \u003cp\u003eIHC was performed on 50 formalin-fixed, paraffin-embedded NSCLC tumor tissues. The IHC results were analyzed semi-quantitatively by using the histochemistry score (H-Score). The IHC technique and the H-Score calculation were as previously reported\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The median of the H-Score was used as the cut-off value to classify the samples into high- or low-expression groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.7 Transwell migration assay\u003c/h2\u003e \u003cp\u003eNSCLC cells were transfected with DLAT-vector, DLAT sequence, si-DLAT#2, si-DLAT#3 plasmids. The migration abilities of transfected NSCLC cells were assessed using transwell chambers, following the previously described methodology\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. After 48h of incubation, cells in the upper chamber were fixed, stained, and then counted under a microscope.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.8 Statistics Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.1.2) if not otherwise specified. The data were visualized by ggplot2. Statistical analyses were performed using SPSS (version 26) in \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003eIHC validation\u003c/span\u003e section. The chi-square test was used to analyze the correlation of categorical data. K-M survival analysis was used to assess the survival of NSCLC. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential and prognostic analysis of CRGs\u003c/h2\u003e \u003cp\u003eGene expression profiles and clinical information of 99 normal lung tissues and 932 NSCLC tissues were obtained from the TCGA database. Differential analysis was performed based on the expression of 19 CRGs, and 17 differentially expressed CRGs were determined after comparing normal and tumor tissues. Among them, 7 CRGs (LIAS, LIPT2, DLD, DLAT, PDHA1, CDKN2A, GCSH) were up-regulated in NSCLC tissues, while 10 CRGs (NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, PDHB, MTF1, GLS, DLST) were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). 799 of 932 NSCLC cases had complete metastasis data, and we grouped them into two subgroups (M0 for no metastasis and M1 for metastasis). 4 CRGs (LIAS, DLAT, PDHB, DLST) were over-expressed in the M1 subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, 8 CRGs (DLAT, DLST, ATP7A, CDKN2A, NLRP3, SLC31A1, DLD, LIPT1) were significantly associated with prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, DLAT and DLST were differentially expressed in the M1 subgroup and also remarkably correlated with prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cuproptosis clusters based on CRGs\u003c/h2\u003e \u003cp\u003eTo further understand the relationship between CRGs and NSCLC, two cuproptosis clusters were identified (cluster A and cluster B) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The survival of cluster A was significantly better than cluster B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Little difference in the clinical parameters between clusters A and B were found, but the expression of CRGs were higher in cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). It can be speculated that the survival difference between clusters A and B was explained by the differential CRGs expression. This further supported that CRGs played a certain role in NSCLC. PCA analysis indicated that the two cuproptosis clusters could be distinguished, but there was partial overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), considering to be caused by our selection of only two principal components. The ssGSEA demonstrated that all immune cells, except CD8\u0026thinsp;+\u0026thinsp;T cells and immature dendritic cells, had lower infiltration degree in cluster A than in cluster B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The heart of the tumor immune response is CD8\u0026thinsp;+\u0026thinsp;T cells, and this suggested that CRGs may influence the progression and prognosis of NSCLSC by regulating immune cell infiltration apart from CD8\u0026thinsp;+\u0026thinsp;T cells. However, which and how CRGs regulate the relevant immune infiltrating cells requires further study. GSVA revealed that the two subclusters were significantly different on the functional enrichment, which may account for the prognostic difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Cluster A was enriched in intraciliary transport, RNA methylation, and metabolism of fatty acids and amino acids. While cluster B was enriched in ligand-receptor interactions and collagen metabolism. Total 185 DEGs were identified in the two subclusters. They were mainly enriched on immunological functions, IL-17 signaling, ECM-receptor interactions and local adhesion pathways (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). These functions and pathways were mainly focused on immune response and tumor metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of a prognostic model based on CRGs\u003c/h2\u003e \u003cp\u003eA risk score prognostic model was constructed by Lasso Cox regression analysis and cross-validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The equation is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(risk score= \\left({Exp}_{SERPINE1}\\times 0.143657491357375\\right)+\\left({Exp}_{FAM83A}\\times 0.104404411929857\\right)\\)\u003c/span\u003e\u003c/span\u003e. The risk scores were calculated for each individual according to the formula, and patients were divided into a high- or low-risk group based on the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The genes in the prognostic model were both highly expressed in the high-risk group (\u003cb\u003eSupplementary Figure S2A, B, and C\u003c/b\u003e). The Sankey diagram was used to visualize differences in risk scores and in survival status among the subclusters (\u003cb\u003eSupplementary Figure S2D\u003c/b\u003e). The majority of patients in cluster A were in the low-risk group, and most of them were still alive by the date of investigation. This was consistent with the fact that cluster B had a higher risk score than cluster A significantly (\u003cb\u003eFigure S2E\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the capability of the model to predict prognosis, K-M survival analysis and ROC curve analysis were performed in the training set, TCGA internal testing set and GEO external testing set. Patients in TCGA internal testing set and GEO external testing set were also divided into a high- or low-risk group based on the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eG \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). The survival rates of patients in the low-risk group were significantly higher than those in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The ROC curves indicated that the model has a good predictive capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multivariable Cox regression was visualized by nomogram, which showed that the risk score was an independent prognostic factor for NSCLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). The calibration curves showed that the actual curves were close to the predicted curves, which confirmed the encouraging ability of the constructed model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of tumor microenvironment and drug sensitivity based on high- and low-risk groups\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment (TME) is a complex environment for tumor cells to survive and progress, including immune cells, stromal cells, various signaling molecules and extracellular matrix (ECM). The stromal and ESTIMATE scores were significantly higher in the high-risk group, while the immune score was not significantly different in the two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The tumor purity was negatively correlated with ESTIMATE scores. Therefore, we speculated that the low survival in the high-risk group was a consequence of stromal cells in TME. Previous studies have demonstrated that key components of stroma in TME promote tumor cell growth and metastasis, and also influence immunotherapy and drug sensitivity of tumors. Differential analysis of three common immune checkpoint genes showed that the expression of CD274 (known as PD-L1) was significantly higher in the high-risk group, while PDCD1 and CTLA4 were not. This illustrated that patients in the high-risk group might achieve greater clinical benefits by using immunotherapy with anti-PD-L1 immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). It also meant that our model may provide information for screening the population for immunotherapy benefits. The half maximal inhibitory concentration (IC50) of common antitumor drugs were calculated in the two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The smaller the IC50 was, the greater the sensitivity of the tumor to the drug. Most drugs were more sensitive in the high-risk group, including common chemotherapy drugs such as gemcitabine and paclitaxel-like drugs, as well as common targeted drugs, like tyrosine kinase inhibitors (TKIs). It can be seen that NSCLC in the high-risk group was more sensitive to chemotherapy and targeted therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCombined diagnosis and single-gene batch correlation analysis for DLAT and DLST\u003c/h2\u003e \u003cp\u003eProtein lipoylated was a crucial process in cuproptosis, and DLAT and DLST were proven to be essential lipoylated protein genes. In addition, they were differentially expressed both in NSCLC and in the M1 subgroup, and high expression of them was associated with poorer prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). We thus wondered whether the combined diagnosis of DLAT and DLST could predict metastasis in NSCLC. A model was constructed to calculate the probability of metastasis in NSCLC. The AUCs of DLAT and DLST was 0.555 and 0.446 respectively, while the AUC increased to 0.578 after combining the DLAT and DLST. This proved that the combined diagnosis of DLAT and DLST can predict the metastasis in NSCLC to some extent. However, it is unclear how they affect metastasis and prognosis in NSCLC because the functions of them are poorly studied. Genes in the top 1000 correlated with DLAT and DLST were obtained respectively, and GO enrichment analysis was performed on them. DLAT-correlated genes were involved in ATP hydrolysis, GTPase binding, ribonucleoproteins (RNPs) biogenesis, and RNA splicing. DLST-correlated genes were involved in ATP hydrolysis, RNA helicase activity, RNPs biogenesis and RNA splicing, (\u003cb\u003eFigure S3\u003c/b\u003e). Their involvement in ATP metabolic was consistent with the fact that tumor cells require high energy for metastasis. In addition, they were involved in RNPs biogenesis and RNA splicing, which could also affect tumor cell metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIHC validation for DLAT and DLST\u003c/h2\u003e \u003cp\u003eImmunohistochemical validation was performed on 50 NSCLC samples, 4 cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The samples were divided into high- or low-expression group based on the median H-Score of DLAT and DLST. The clinicopathological characteristics and their correlation with the expression of DLAT and DLST are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. DLAT expression was significantly correlated with the M stage, but it was not correlated with gender, age, T and N stages, pathology, and brain metastasis status. DLST expression was significantly correlated with T, N, and M stages, pathology, and brain metastasis status, but not age and gender. DLAT and DLST expression were both significantly associated with metastasis, and univariate and multifactorial logistic regression analyses showed that gender, expression of DLAT and DLST were all independent predictive factors for metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Males in gender, high DLAT-expressing, and low DLST-expressing of NSCLC were more likely to have metastasis. This aligns precisely with our bioinformatics analysis results, indicating a significantly increased expression of DLAT within the M1 subgroup. However, the IHC suggested that DLST expression was lower in the M1 subgroup, which probably resulted from limitations in sample source. That is, DLAT and DLST expression can predict metastasis in NSCLC.\u003c/p\u003e \u003cp\u003e \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 features of 50 NSCLC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDLAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eDLST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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 \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge/ Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eUnivariate and multifactorial logistic analysis of metastasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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 \u003cp\u003e3.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.052\u0026ndash;12.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.015\u0026ndash;22.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.376\u0026ndash;14.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.022\u0026ndash;15.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u0026ndash;0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032\u0026ndash;0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\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\u003eIn addition, the expression of DLST showed a correlation with brain metastasis status (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05), so DLST can help clinicians identify high-risk brain metastasis patients and thus prevent brain metastasis in advance. The middle PFS was 14 and 25 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.134) for the DLAT high and low-expression groups, and 22 and 24 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.226) for the DLST high and low-expression groups. This means that both DLAT and DLST were not significantly different in survival. Among the 24 NSCLC patients with immunotherapy, the middle PFS for DLAT high and low expression groups were 7 and 24 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), and the middle PFS for DLST high and low expression groups were 15 and 12 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.552). This suggested that DLAT might predict NSCLC immunotherapy efficacy, consistent with a fact that DLAT expression was significantly correlated with PD-L1 expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDLAT expression affects cell migration in NSCLC cells\u003c/h2\u003e \u003cp\u003eFrom the transwell assay, the number of migration cells were significantly increased in A549 and HCC827 cells transfected with oeDLAT compared to cells transfected with the vector control (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Subsequently, we knocked down DLAT in A549 and HCC827 cells, and assessed the migrated cell number in these cell lines. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, the knockdown of DLAT led to a reduction in cell migration in both A549 and HCC827 cells compared to the vector control-transfected cells. Notably, the knockdown of DLAT remarkably impaired the capabilities of migration in A549 and HCC827 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAn illustrative case of DLAT for predicting immunotherapy efficacy in NSCLC\u003c/h2\u003e \u003cp\u003eTo demonstrate visually the therapeutic response of NSCLC lesions to anti-PD-L1-immunotherapy, we examed an illustrative case who showed a low DLAT expression. A 61-year-old NSCLC patient received anti-PD-L1-immunotherapy. CT showed the lung tumor size of 8.31\u0026times;6.17 cm before the patient received immunotherapy. 9 months after treatment, CT revealed the tumor size decreased to 4.70\u0026times;3.93 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The patient's condition was assessed for PR according to the iRECIST evaluation criteria. This further demonstrates that low DLAT expression predicts good immunotherapy efficacy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have revealed that Cu is closely involved in cell proliferation, tumor metastasis, angiogenesis and the remodeling of tumor microenvironments via various molecular mechanisms\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Moreover, Cu can regulate the expression of PD-L1 in tumor cells, thus affecting anti-PD-L1 antitumor therapy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The above studies all illustrate the importance of Cu in tumor cells. Recently, it was found that excess intracellular copper can trigger a new form of cell death by targeting lipoylated proteins in the TCA cycle. This means that Cu combines with lipoylated proteins, causing aggregation of lipoylated protein and loss of iron-sulfur cluster protein. It then leads to proteotoxic stress and ultimately cell death\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This particular type of cell death is known as cuproptosis and is receiving increasing attention. However, the prognostic value of CRGs in NSCLC is unknow. DLAT and DLST are essential lipoylated protein genes in the TCA cycle. As shown in the part of our bioinformatics analysis, DLAT and DLST played an essential role both in the metastasis and prognosis of NSCLC. However, the result is only based on public databases and still need to be supported by experiments and clinical data. Therefore, our study not only explored the prognostic value of the CRGs-related prognostic model but innovatively analyzed the value of DLAT and DLST in metastasis for NSCLC.\u003c/p\u003e \u003cp\u003eIn our study, we identified two cuproptosis clusters based on CRGs, where patients in cluster A had a significantly better prognosis than cluster B. CRGs in NSCLC are upregulated in the majority of cluster A, with LIPT1 most notably. It has been reported that LIPT1 is upregulated in melanoma and is an independent favorable prognostic indicator\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.Therefore, LIPT1 may play a similar role in inhibiting tumor progression in NSCLC. In addition, 185 DEGs were identified in these two clusters. The above results initially confirm the potential value of CRGs in NSCLC.\u003c/p\u003e \u003cp\u003eOur risk score prognostic model ultimately identified two genes, SERPINE1 and FAM83A. SERPINE1, an inhibitor of plasminogen activator, was expected to protect against tumor proliferation by inhibiting plasminogen activator activity. However, several studies have confirmed that SERPINE1 was significantly upregulated in a variety of malignancies, including glioblastoma, esophageal squamous carcinoma, breast cancer, gastric cancer, bladder cancer, and oral squamous carcinoma. It is also an important marker of poor prognosis in various malignancies\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Therefore, Miguel et al. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e concluded that SERPINE1 was responsible for the poor prognosis by multiple signaling pathways which were independent of the plasminogen activator (PA) system. FAM83A, a family member A gene with sequence similarity 83, was identified as a tumor-promoting gene that is upregulated in several malignancies and was significantly associated with poor prognosis in tumors\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In addition, overexpression of FAM83A may affect PD-L1 expression and resistance to EGFR-TKI drugs, which may affect the efficacy of immunotherapy and targeted therapy\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. It was evident that the two genes included in the risk model were closely related to the progression and prognosis of tumors. For this reason, it is well-supported to use them to construct the prognostic models.\u003c/p\u003e \u003cp\u003eNSCLC is a highly heterogeneous tumor with relatively complex treatment and highly different survival rates. The treatment and prognosis of NSCLC differ markedly by individual, even if identical in the stage. In clinical practice, molecular pathology test results of NSCLC are used to predict the development of NSCLC and help clinicians to individualize treatment, but they are not completely accurate. We classified NSCLC into high- and low-risk groups based on the risk prognostic score model. The survival of the low-risk group is remarkably better than the high-risk group. Moreover, risk score was an independent risk factor for NSCLC prognosis. All of these supported the potential clinical value of the risk score. In tumor tissues, the purity of tumor cells was lower in the high-risk group for NSCLC, while the immune infiltration did not differ significantly compared with the low-risk group. It suggested that the poorer prognosis in the high-risk group may be explained by the stromal component in the tumor tissues. Some components of the stroma, such as cytokines produced by cancer-associated fibroblasts (CAF) and tumor-associated macrophages (TAM), promote infiltration and metastasis of tumor cells \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In general, an increase in stromal components, such as extracellular matrix (ECM), may increase drug resistance in tumor cells. However, our study showed that the high-risk group was more sensitive to some common antitumor drugs. It indicates that CRGs may affect the genetics or epigenetics of tumor cells rather than TME, resulting in higher drug sensitivity in the high-risk group. PD-L1 expression was significantly higher in the high-risk group, suggesting that the high-risk group may be more sensitive to immunotherapy. In summary, the model we constructed could be a valuable tool for risk classification of NSCLC. It has a good ability to predict the prognosis of NSCLC and to guide clinical individualized treatment.\u003c/p\u003e \u003cp\u003eCurrently, most of the metastases in NSCLC are clinically diagnosed through imaging. However, imaging is difficult to confirm early metastases and there is also a certain rate of false positives and false negatives. This leads to the clinicians' failure to detect the sign of metastasis in time, so there is a demand for higher diagnostic efficacy of metastasis in clinical practice. Immunotherapy based on immune checkpoint inhibitors led to a leap forward in the treatment of NSCLC, increasing the 5-year survival rate of NSCLC to 16% for the first time, but only some of the patients benefit from immunotherapy. PD-L1 testing has been included in the treatment guideline for NSCLC for screening potential beneficiaries of immunotherapy. However, PD-L1 testing is still an effective but imperfect tool and not an absolute marker for immunotherapy due to various limitations\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we used bioinformatics to discover that DLAT and DLST play an important role in metastasis in NSCLC. However, there are no studies on their role in tumor metastasis. Single-gene bulk correlation analysis indicated that the functions of them have a large partial overlap and were mainly focused on the processes of RNA splicing and processing and synthesis of RNPs. It has been reported that dysregulation of the RNA splicing was associated with increased invasion, angiogenesis, metastasis, and drug resistance of cancer cells\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In addition, RNPs may also influence tumor progression through diverse mechanisms. Interestingly, the spliceosome is one of the members of RNPs. Moreover, growing evidence strongly identifies an essential link between the EMT program and Ribonucleoprotein (RNP) biogenesis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which leads to tumor cell migration, invasion, and eventual metastasis. Previous studies shows that high expression of DLAT is associated with metastasis in liver and colon cancer, predicting a poor prognosis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Depletion and inhibition of DLST decrease invasion and metastasis of triple-negative breast cancer (TNBC) cells\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In conclusion, these studies provide a theoretical basis for our study that explores the value of DLAT and DLST in NSCLC metastasis.\u003c/p\u003e \u003cp\u003eThe results of IHC analysis were generally consistent with the results of the bioinformatic analysis. DLAT and DLST expressions were significantly correlated with metastasis and have the potential to be markers for predicting metastasis in NSCLC. Moreover, both DLAT and DLST expression were independent predictive factors for metastasis in NSCLC. In the Transwell migration assay, we observed that the overexpression of DLAT significantly increased the migratory capacity of cells in vitro, while the knockdown of DLAT effectively inhibited cell migration. These results contribute to a better understanding of the molecular mechanisms underlying the metastatic process in NSCLC and may have implications for the development of targeted therapies. However, these results are limited to in vitro experiments. To further explore the molecular mechanisms of DLAT, additional in vivo experiments and molecular pathway studies are needed.\u003c/p\u003e \u003cp\u003eNSCLC patients have a high rate of brain metastasis, which response poorly to conventional drug therapy due to the presence of the blood-brain barrier. Fortunately, we found that DLST expression has the potential to predict brain metastasis, so clinicians may expect to selectively take measures to prevent brain metastasis based on DLST expression, thus making treatment more individualized. This may enable more patients to benefit from brain radiotherapy. Furthermore, although our study demonstrated that DLAT and DLST were strongly associated with metastasis, the IHC results showed that none of their associations were reflected in the prognosis. In our study, DLAT expression significantly correlated with PD-L1, and low DLAT expression indicated better immunotherapeutic efficacy, while DLST did not. It has been confirmed that CRGs could predict immunotherapy efficacy for breast cancer\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. It may provide preliminary evidence for the potential of DLAT in predicting the efficacy of anti-PD-L1 immunotherapy for NSCLC. Therefore, we need more in-depth studies to explore the value of DLAT and DLST in tumor cells and to determine their accuracy and reliability as potential markers.\u003c/p\u003e \u003cp\u003eIn conclusion, the prognostic model constructed based on CRGs has a good predictive ability for the prognosis of NSCLC, and may provide a reference for the clinical interventions. DLAT may play a role in predicting immunotherapy efficacy. In addition, DLAT and DLST are expected to serve as markers of metastasis in NSCLC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from the National Natural Science Foundation of China (Grant Number 82002869).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe authors declare that the research was conducted in the absence of any financial or non-financial interests that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe information of TCGA and GEO databases can be found at the following website:\u0026nbsp;https://portal.gdc.cancer.gov/,\u0026nbsp;https://www.ncbi.nlm.nih.gov/geo/. The data that support the findings of this study are available in this article and its supplementary files. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the reviewers for their helpful comments on this article, as well as the TCGA and GEO databases for kindly providing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYG and WC designed the study and contributed to the experiments of this study. HM and YL performed the statistical analysis and wrote the manuscript. TW provided technical guidance. YG and WC participated in the manuscript revision. All authors contributed to the article and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the Ethics Committee of Jiangsu Province Cancer Hospital. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Jiangsu Province Cancer Hospital (Grant Number江苏省肿瘤医院伦理委员会2023\u0026nbsp;科-快\u0026nbsp;063).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. \u003cem\u003eCA Cancer J Clin. \u003c/em\u003e2022;72(1):7-33.\u003c/li\u003e\n\u003cli\u003eXie J, Yang Y, Gao Y, He J. 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Comprehensive analysis of cuproptosis-related genes and tumor microenvironment infiltration characterization in breast cancer. \u003cem\u003eFront Immunol. \u003c/em\u003e2022;13:978909.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cuproptosis, non-small cell lung cancer, prognosis, metastasis, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-3849451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo reveal the clinical value of cuproptosis-related genes on prognosis and metastasis in non-small cell lung cancer.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eGene expression profiles and clinical information of non-small cell lung cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The data were grouped into training set, internal testing set, and external testing set. A risk prognostic model was constructed by Lasso-Cox regression analysis. Hub genes were identified and evaluated using immunohistochemistry and the Transwell migration assay in 50 clinical patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 17/19 cuproptosis-related genes were differentially expressed in tumors, 8 were significantly associated with prognosis, and 4 were markedly associated with metastasis. A risk model based on two cuproptosis-related genes was constructed and validated for predicting overall survival. The risk score was proven to be an independent risk factor for the prognosis of non-small cell lung cancer. DLAT and DLST, key genes in cuproptosis, were proven to be associated with non-small cell lung cancer prognosis and metastasis. Immunohistochemistry showed that their expression significantly predicted metastasis but failed to predict prognosis in non-small cell lung cancer patients. The transwell migration assay further increased the cellular reliability of our findings.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe cuproptosis-related genes prognostic model effectively predicted the prognosis of non-small cell lung cancer. DLAT and DLST may serve as predictive markers for metastasis in non-small cell lung cancer.\u003c/p\u003e","manuscriptTitle":"Construction of a prognostic model based on cuproptosis-related genes and exploration of the value of DLAT and DLST in the metastasis for non-small cell lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-22 14:52:31","doi":"10.21203/rs.3.rs-3849451/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":"56f85e59-4caf-4c46-9493-f538adb01b3b","owner":[],"postedDate":"January 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-28T05:14:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-22 14:52:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849451","identity":"rs-3849451","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}