Identification of promising lung cancer drug targets from human plasma proteins: Mendelian randomization, single-cell RNA sequencing analysis

Identification of promising lung cancer drug targets from human plasma proteins: Mendelian randomization, single-cell RNA sequencing analysis | 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 Identification of promising lung cancer drug targets from human plasma proteins: Mendelian randomization, single-cell RNA sequencing analysis Xiao-dong Shao, Zhou-lin Miao, Wei-jie Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6514191/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background: Lung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for lung cancer. Method : Protein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing. Result : We identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8（OR = 0.87，95%CI:0.78-0.97，p=0.013）had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A（OR =1.20，95%CI:1.041-1.38，p=0.012) exhibited the most significant MR result . Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. In addition, the researchers used the DSigDB database to predict potentially effective intervening drugs and obtained nine possible drugs. Molecular docking showed that the drugs bind very much to the proteins . KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells. Conclusion : The present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs. lung cancer mendelian randomization drug target Plasma protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Lung cancer(LC) is currently the leading malignant tumor in the world and the primary cause of cancer-related deaths [ 1 ] . According to expert predictions, lung cancer may become a major public health concern globally by 2035 [ 2 ] . Current therapeutic strategies for the treatment of this condition consist of surgical resection, chemotherapy, targeted therapy, and radiotherapy. Nevertheless, despite the range of treatment options available, the prognosis remains poor [ 3 ] . Despite the large amount of research invested in the pathogenesis of lung cancer, the number of valuable genes that have been accessed is still in the minority due to the small size of the studies and their susceptibility to known causal factors. It is imperative to understand the genomic and protein structure of lung cancer in order to comprehend its pathogenesis and to develop personalised, targeted therapies. It is important to note that a significant proportion of lung cancer patients cannot be matched to targeted drugs and that acquired resistance is common after treatment [ 3 ] . In recent years, the combination of novel nanocarriers and targeted therapies has facilitated the development of multimodal targeted therapies for lung cancer [ 7 – 9 ] . The advent of high-throughput drug screening and personalized precision medicine, founded upon organ-on-a-chip technology, has concomitantly engendered a multiplicity of opportunities for the treatment of lung cancer [ 10 – 13 ] . Nevertheless, the overall 5-year survival rate for lung cancer patients remains suboptimal. Consequently, there is an urgent need to identify new therapeutic targets for lung cancer. Proteins function as a critical link between genetics and phenotypes, and complex protein-protein interactions that mediate signaling pathways and biological processes play a significant role in the underlying causes of lung cancer. Simultaneously, whether physically proximal to protein-coding genes (cis-) or located anywhere else in the genome (trans-), these interactions broadly influence protein structure and function, which is of great significance to complex diseases. Proteins circulating in the blood provide fundamental information about human health and can serve as potential biomarkers and drug targets. They are the primary targets of FDA-approved drugs [ 14 ] . Genome-wide association studies (GWAS) of plasma proteins have demonstrated the presence of genetic variants associated with these proteins. Such variants are known as "protein quantitative trait loci (pQTLs)." The advent of next-generation proteomics technologies has enabled the identification of unusual protein expression, thus facilitating further exploration of potential biomarkers and cancer therapeutic targets and contributing to our understanding of the underlying mechanisms of the disease. Despite the identification of numerous risk loci associated with lung cancer by GWAS, the genetic basis of this disease is still not fully understood, and GWAS data cannot be directly used for drug development [ 15 , 16 ] . Mendelian randomization (MR) analyses same leverage genetic (SNP) data from GWAS databases to infer causal relationships between exposure and outcome variables and discern associations between proteins and phenotypes. These genetic instruments are usually independent of confounders and are not influenced by environmental, behavioral, psychological, or socio-economic influences after birth. Recent studies have identified the utility of MR in the identification of drug targets. This approach has been demonstrated to facilitate the identification of causal effects of plasma proteins on lung cancer while simultaneously reducing experimental bias and confounding factors. Moreover, MR has yielded encouraging results in identifying potential biomarkers and evaluating risk, protective factors, and biological mechanisms associated with lung cancer. Therefore, the objective of the current study was to identify potential plasma proteins associated with lung cancer. Subsequent analysis of their protein-protein interactions (PPI) and regulatory roles in key cellular pathways enabled the identification of optimal targets for novel interventions. Subsequently, enrichment analysis was employed to ascertain the intricate pathogenesis of these lung cancer-associated plasma proteins. In order to ensure the robustness of the results, a sensitivity analysis and a reverse MR were performed. The integration of MR, drug prediction, gene enrichment analysis, and the construction of protein-protein interaction (PPI) networks are expected to provide valuable guidance for developing more effective and targeted therapeutic approaches and new directions for discovering new therapeutic targets for lung cancer. Through single-cell RNA sequencing analysis, it is expected that the potential causal role of specific plasma proteins in the pathogenesis of lung cancer will be illustrated. Method The association between plasma proteins and LC was explored via MR, leveraging pooled data from 4907 proteomics studies [ 17 ] . Figure 1 depicts the overall schematic of this study. In addition, analysis of filtered proteins was used to assess druggability and prioritize therapeutic targets. Exposure data We obtained human 4907 plasma proteins from a database (deCODE genetics | a global leader in human genetics) and conducted screening procedures [ 17 ] . All 4907 protein data were derived from 35,559 Icelandic individuals. Exposure data were subject to the following conditions: (a) SNP selection. SNP including both cis - SNPs and trans - SNPs with a significance level of P < 5×10 − 8 ,were selected; (b) Independent Associations. To ensure independent associations, linkage disequilibrium (LD) aggregation r 2 < 0.001 and genetic distance was set to 10,000 kb, which was required. This investigation was guided by three key principles: (1) the relevance criterion: The IVs must exhibit notable associations with the exposure variables. This is crucial as it validates the use of IVs to represent the exposure in the analysis; (2) the independence criterion: The IVs should remain independent of any potential confounders, whether they are known or unknown; (3) the exclusion restriction criterion: The IVs exert their influence on the outcome exclusively through the exposure entities. The application of this principle facilitates the identification of the causal pathway from the exposure to the outcome via the instrumental variables (IVs). In order to avoid weak instrumental variable bias, genetic variables with an F statistic of less than 10 were excluded. Access to outcome data For the main results, we found summary genetic association data for lung cancer from the IEU Open GWAS project ( https://gwas.mrcieu.ac.uk/ ). We employed the search term ‘lung cancer’ to explore datasets. Through the application of inclusion and exclusion criteria, we selected studies that met our research objectives. Ultimately, we obtained a dataset with the ID: ebi-a-GCST90018875. This dataset consisted of individuals of European ancestry, comprising 3791 lung cancer cases and 489012 controls. Mendelian Randomization analysis We performed MR analysis based on cis and trans pQTL from deCODE. Firstly, the exposure and outcome cohorts were predominantly composed of individuals of European ancestry, thereby minimizing potential biases arising from population distribution disparities. By employing the “TwoSampleMR” R package in R version 4.4.2 ( https://github.com/MRCIEU/TwoSampleMR ), we designated plasma proteins as the exposure variable and lung cancer as the outcome variable to execute the primary Mendelian Randomization (MR) analysis. We utilized five distinct methods to estimate the causal effects: MR - IVW (Inverse Variance Weighting), MR - Egger, weighted median, simple mode, and weighted mode. Among these, the principal method for establishing causation was inverse variance weighting (IVW). Using multiple methods enhanced the robustness and comprehensiveness of our causal inference, with IVW serving as the cornerstone in determining the most reliable estimates of the causal relationships between plasma proteins and lung cancer. This approach allowed us to systematically explore and quantify the potential causal links, providing a more in-depth understanding of the underlying biological mechanisms and potential implications for disease prevention and treatment strategies. Sensitivity analysis The potential presence of pleiotropy was further explored using the MR-Egger method to assess its potential impact. In cases where the MR-Egger intercept value was less than 0.05, we classified these protein-disease associations as being influenced by horizontal pleiotropy. Additionally, we employed Cochran's test to evaluate the potential heterogeneity of MR estimates derived from both the MR-Egger and IVW methods (with a significance level of value < 0.05). Proteins exhibiting horizontal pleiotropy and heterogeneity were excluded from all subsequent follow-up analyses. This exclusion was crucial to ensure the integrity and reliability of our results, as pleiotropy and heterogeneity can introduce confounding factors and undermine the accuracy of causal inference. To validate the absence of reverse causality, we implemented reverse Mendelian randomization for the disease-associated proteins identified in the screening process. We set the thresholds p 1 and p 2 to be lower than 5 ×10 − 8 and considered the p-value, the result of IVW, as the primary metric for observation. The results of this analysis were then visualized to provide a clear and intuitive understanding of the relationships between the variables. This step was essential in strengthening the causal interpretation of our findings and ensuring that the observed associations were not due to reverse causation, thereby enhancing our study's overall validity and credibility. Disease-associated protein filtering The protein filtering method must satisfy the following conditions: 1. The p-value of IVW < 0.05; 2. The OR of the five Mendelian randomization calculations are all simultaneously greater than one or less than 1; and 3. The pval of pleiotropy is more significant than 0.05. Protein - Protein Network Analysis In order to delve into the interactions between potential therapeutic targets and the underlying mechanisms of LC, a Protein - Protein Interaction (PPI) analysis was conducted. The PPI analysis was used through the STRING database ( https://string-db.org ) and Cytoscape software, with a minimum required interaction score of 0.4. Sort the proteins by the number of connected nodes and take the top ten. The top ten proteins with the highest number of connected nodes were selected for the next step in order to explore druggability. Candidate Drug Prediction To evaluate the druggability of the identified target proteins, we initiated the process by predicting potential candidate drugs using the Drug Signatures Database (Dsigdb, https://dsigdb.tanlab.org/ ). Dsigdb is a comprehensive database linking medications and other chemical compounds to their target genes. This resource provides a valuable platform for exploring the relationships between drugs and genes, enabling us to identify potential drugs that can interact with the target proteins of interest. By leveraging the information in Dsigdb, we can narrow down the list of candidate drugs and prioritize them for further investigation, which is crucial for developing effective therapeutic strategies. Gene-drug pathway enrichment was performed using the Enrichers platform. Drug structure data were obtained from the PubChem Compound Database ( https://pubchem.ncbi.nlm.nih.gov/ ), and the corresponding IDs are shown. Protein structure data were obtained from the PDB (Protein Data Bank, http://www.rcsb.org/ ). In this study, a novel blind docking tool named CB - Dock2 was employed. The primary objective of CB - Dock2 is to enhance the accuracy of docking procedures. It achieves this by predicting the binding region of a given protein. The ligand molecule can bind spontaneously to the receptor protein if its binding energy is negative. A lower binding energy is indicative of a more tightly binding interaction between the two molecules. Single-cell RNA sequencing analysis Single-cell RNA sequencing (scRNA-seq) data for lung tissue was obtained from the Panglao DB database ( https://panglaodb.se/ ). This database is designed to meet the needs of the scientific community, with a focus on single-cell RNA sequencing experiments in mice and humans [ 18 ] . The dataset was then searched using the 'Samples' module. Filter criteria were set to 'Human and mouse' and 'Tissue.' The selection was based on the dataset that contained the most cell types. Results Identified 46 plasma proteins associated with LC by MR In the present study, data from GWAS and deCODE were employed. Through MR analysis, following the exclusion of proteins exhibiting pleiotropy, 46 plasma proteins were successfully identified as being causally associated with lung cancer(Supplementary Table S1). PHGDH(OR = 1.01, 95%CI:1.00-1.20, p = 0.049)、MMP19(OR = 1.13, 95%CI:1.00-1.23, p = 0.049)、SMPDL3A(OR = 1.12, 95%CI:1.00-1.24, p = 0.045)、PAPPA(OR = 1.01, 95%CI:1.00-1.20, p = 0.04)、SLC5A8(OR = 1.09, 95%CI:1.00-1.18, p = 0.040)、SCARB2(OR = 1.49, 95%CI:1.001, p = 0.047)、BIRC7(OR = 1.15, 95%CI:1.01–1.30, p = 0.040)、LGALS3BP(OR = 1.10, 95%CI:1.01–1.20, p = 0.036)、ANGPTL4(OR = 1.13, 95%CI:1.01–1.27, p = 0.038)、SEMA4B(OR = 1.30, 95%CI:1.01–1.67, p = 0.042)、TNS4(OR = 1.18, 95%CI:1.01–1.39, p = 0.038)、GRAMD1C(OR = 1.12, 95%CI:1.01–1.24, p = 0.032)、CPOX(OR = 1.14, 95%CI:1.01–1.28, p = 0.032)、TCTN2(OR = 1.15, 95%CI:1.01–1.31, p = 0.032)、TMCC3(OR = 1.11, 95%CI:1.01–1.23, p = 0.027)、IFIT2(OR = 1.24, 95%CI:1.01–1.51, p = 0.038)、SECTM1(OR = 1.14, 95%CI:1.01–1.28, p = 0.028)、CD33(OR = 1.08, 95%CI:1.015–1.15, p = 0.016)、WFIKKN2(OR = 1.01, 95%CI:1.017–1.19, p = 0.017)、APOF(OR = 1.19, 95%CI:1.02–1.40, p = 0.030)、CLN5(OR = 1.25, 95%CI:1.02–1.54, p = 0.031)、NTF3(OR = 1.31, 95%CI:1.024–1.68, p = 0.032)、FAIM(OR = 1.19, 95%CI:1.03–1.31, p = 0.018)、B3GALT1(OR = 1.54, 95%CI:1.03–2.30, p = 0.036)、TMEM70(OR = 1.28, 95%CI:1.04–1.58, p = 0.022)、DGCR2(OR = 1.57, 95%CI:1.04–2.39, p = 0.033)、IL36A(OR = 1.20, 95%CI:1.041–1.38, p = 0.012)、LRRC24(OR = 1.47, 95%CI:1.046–2.077, p = 0.027)、CCNA2(OR = 1.43, 95%CI:1.01–1.93, p = 0.019)、HSD17B11(OR = 1.52, 95%CI:1.066–2.17, p = 0.021)、POLR3F(OR = 1.56, 95%CI:1.07–2.27, p = 0.020)were associated with an increased risk of LC. Notably, IL36A exhibited the most significant MR result. In contrast, RIC3(OR = 0.042, 95%CI:0.55–0.99, p = 0.042)、DEFB135(OR = 0.76, 95%CI:0.61–0.96, p = 0.019)、CLEC4M(OR = 0.77, 95%CI:0.61–0.98, p = 0.031)、COL2A1(OR = 0.78, 95%CI:0.64–0.95, p = 0.016)、BLVRB(OR =, 95%CI:, p = 0.039)、DEFA1(OR = 0.82, 95%CI:0.67–0.99, p = 0.042)、JPH1(OR = 0.82, 95%CI:0.67-1.00, p = 0.048)、MMP8(OR = 0.87, 95%CI:0.78–0.97, p = 0.013)、SERPING1(OR = 0.89, 95%CI:0.81–0.98, p = 0.022)、KIR2DL3(OR = 0.90, 95%CI:0.82–0.99, p = 0.025)、PLTP(OR = 0.92, 95%CI:0.84-1.00, p = 0.049)、ACP1(OR = 0.92, 95%CI:0.84–0.99, p = 0.036)、KDR(OR = 0.92, 95%CI:0.85-1.00, p = 0.048)、CLPS(OR = 0.92, 95%CI:0.86-1.00, p = 0.037)、GNLY(OR = 0.93, 95%CI:0.87-1.00, p = 0.041) were associated with an decreased risk of LC. Among them, MMP8 showed the most significant protective effect (Fig. 2A-D). Reverse Mendelian randomisation results To further explore issue of reverse causality, we subsequently conducted a MR analysis for the 46 potential plasma proteins identified in the primary analysis. However, we found that for the IVW method, the p - value was less than 0.05 between COL2A1, MMP19 and LC. This indicates a reciprocal causal relationship between lung cancer and two proteins. Simultaneously, the incidence of lung cancer has a negative impact on the expression of COL2A2 and MMP19. Therefore, further visualization of the results of the reverse Mendelian randomization is necessary (Fig. 3A-D). Exploring the Biological Significance by Enrichment Analysis Enrichment analysis, specifically through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, provides a powerful approach to comprehensively understanding the biological functions, metabolic pathways, and interactions of similarly expressed proteins. The GO annotation results for the top 10 proteins revealed notable enrichments. In the Biological Process (BP) category, lipid transport was the central process that was significantly enriched. Regarding the Cellular Component (CC), the collagen-containing extracellular matrix was the prominent feature. For the Molecular Function (MF) of these top 10 proteins, lipid transporter activity, cholesterol binding, and sterol binding were the main molecular functions involved. Upon conducting the KEGG enrichment analysis, it was discovered that these proteins were predominantly associated with several key pathways. These included Porphyrin metabolism, Cholesterol metabolism, and the PPAR signaling pathway. These findings suggest that these proteins play crucial roles in lipid-related biological processes and are involved in specific metabolic and signaling pathways, which may have important implications for understanding the underlying mechanisms of the biological phenomena under investigation. This knowledge can further guide future research to explore the detailed molecular mechanisms and potential therapeutic targets related to these pathways and functions. Construction of protein-protein interaction networks To enhance our comprehension of the potential biological relationships among these proteins and their functions that impact lung cancer, a protein-protein interaction (PPI) network was constructed for the important proteins that were identified through the process of Mendelian randomization (Fig. 5A). Importing the PPI network data into Cytoscape, we identified 10 proteins (SERPING1, LGALS3BP, IFIT2, PHGDH, PLTP, KDR, SCARB2, ANGPTL4, MMP8, CD33) as core proteins(Fig. 5B). Figure B shows that KDR and SERPING1 are the key core proteins. In the figure, the greater the number of protein interactions a protein has, the heavier its color appears. Prediction of Candidate Drug This study used the DSigDB database to predict potentially effective interventional drugs. The drugs found to be the most promising targets were KDR, MMP8,, ANGPTL4, PLTP, SCARB2, and PHGDH. Idocyklin was the most relevant and meaningful drug related to KDR and MMP8. The top 9 potential compounds are shown based on the count value (Table 1). Drug-associated genes were MMP8, ANGPTL4, PLTP, and KDR. The top 9 significantly enriched drugs were Idocyklin, Deguelin, Vandetanib, Hydrogen, 1-phosphatidyl-myo-inositol, SARIN, ascorbic acid, and Dinoprostone. Table 1 Candidate Drug Predicted Using DSigDB database ID Description pvalue p.adjust qvalue Gene ID Count Idocyklin Idocyklin 0.0008 0.046 0.022 KDR/MMP8 2 Deguelin Deguelin 0.0009 0.046 0.022 KDR/ANGPTL4 2 Vandetanib Vandetanib 0.0030 0.046 0.022 KDR/ANGPTL4 2 Hydrogen Hydrogen 0.0038 0.046 0.022 KDR/MMP8 2 1-Phosphatidyl-myo-inositol 1-Phosphatidyl-myo-inositol 0.0041 0.046 0.022 PLTP/KDR 2 SARIN SARIN 0.0058 0.046 0.022 PLTP/SCARB2 2 ascorbic acid ascorbic acid 0.0074 0.0456 0.022 PHGDH/KDR 2 Dinoprostone Dinoprostone 0.0086 0.0458 0.022 KDR/MMP8 2 Molecular Docking CB-dock2 is applied to obtain binding sites and interactions of drug candidates with proteins encoded by the respective genes, and to generate binding energies for each interaction. It is widely accepted that lower binding energies are indicative of enhanced stability. This study performed molecular docking to assess the binding affinity of drug candidates for their targets and, from this, to understand the drug target’s druggability. Unfortunately MMP8, PLTP were not available, so they were failed to predict at this stage. Molecular docking was likewise not possible for vinascore of Hydrogen was > 0, the extensive data for Dinoprostone, the unavailability of structural data for SARIN, and the problematic calculations of 1-phosphatidyl-myo-inositol because of structural issues. Notably, KDR and ANGPTL4 showed significant and stable binding to the drug candidates, suggesting a strong affinity and providing compelling evidence for their druggability. It was decided to molecularly dock and visualize the two proteins separately(Fig. 7A-B). Drug PubChem ID Protein PDB ID Vinascore Vandetanib 3081361 KDR 5EW3 −7.7 ANGPTL4 6EUB −8 ascorbic acid 54670067 PHGDH 7DKM −6.2 KDR 5EW3 −6.2 Idocyklin 54671203 KDR 5EW3 −7.1 Deguelin 107935 KDR 5EW3 −7.6 ANGPTL4 6EUB −7 Single-cell RNA sequencing analysis results The identification code for these samples is SRA653146. Single-cell RNA-sequencing (scRNA-seq) technology offers a means of obtaining cell-specific genetic information, thereby facilitating the elucidation of the detailed functions and roles of target cells. The present study analyzed the scRNA-seq data of lung sample SRA653146 in the Panglao DB database in association with cell clustering results and cell type information. Following rigorous quality control procedures on the data, the t-distributed Stochastic Neighbor Embedding (t-SNE) technique was employed to visualize the single-cell RNA sequencing data. The data downloaded from the Panglao DB database demonstrated that the KDR gene was predominantly expressed in endothelial cells and alveolar type II cells within lung tissues. ANGPTL4 was mainly expressed in fibroblasts, macrophages, and endothelial cells. Therefore, KDR and ANGPTL4 may play an important role in the development of lung cancer.(Fig. 8A-C) Discussion The human protein composition is a key area of research that demands significant attention in the pursuit of identifying new drug targets for lung cancer treatment. Using the largest GWAS data, this study investigated the possible causal relationship between plasma proteins and lung cancer risk by Mendelian randomization. It explored potential therapeutic targets for lung cancer in plasma proteins and the molecular mechanisms of lung cancer pathogenesis. The IL-36 family of cytokines was first identified in 2000, and IL36A is one of the IL-36 family agonists [ 19 ] . In the last twenty years, little has been known about its role in cancer-related diseases. IL-36A is associated with abnormal angiogenesis, which is critical in tumors and key to disease progression and metastasis [ 20 ] . However, the exact mechanism needs to be further investigated. Matrix metalloproteinases (MMP) are zinc-dependent endopeptidases strongly implicated in tumourigenesis by promoting extracellular matrix renewal, cancer cell migration, cell growth, inflammation, angiogenesis, and TME remodeling [ 21 ] . Interestingly, MMP8 is one of the unique members with anti-tumor and anti-metastatic functions. High MMP8 expression is associated with significantly reducing cancer incidence and risk of metastasis and prolonging overall patient survival [ 22 , 23 ] . In breast and skin cancers, MMP8 plays an inhibitory role [ 24 , 25 ] . This is consistent with our findings. But the internal mechanism in lung cancer needs to be further defined. In reverse Mendelian randomization, two proteins including COL2A1、MMP19, and lung cancer were unexpectedly found to be causally related. This has not been reported to elaborate on the intrinsic reason. By the reverse Mendelian randomization study, although false-positive results were found, the possibility of future druggability of other proteins is greatly improved, and the success rate of clinical trials is increased. Enrichment analyses illustrate the potential pathways and functions involved in the screened risk proteins. Next, 10 core genes (SERPING1, LGALS3BP, IFIT2, PHGDH, PLTP, KDR, SCARB2, ANGPTL4, MMP8, CD33) that may be causally related to lung cancer were identified by plotting the PPI network. Among the 10 core genes, SERPING1 and KDR involve the most proteins. However, the functions of SERPING1 and KDR are not fully understood so far. The PPI analysis showed that SERPING1 interacts with KDR. Experts previously found SERPING1 (serine protease inhibitor family G1 ) to be associated with anomalous angioedema, and less studied in oncological diseases. Some experts and researchers have found that SERPING1 is highly expressed in breast cancer patients, is associated with subsequent bone metastasis, and is considered to be a core gene for breast cancer bone metastasis. Peng et al [ 26 ] . suggested that low expression of SERPING1 implies a higher degree of malignancy of prostate lesions, is associated with poorer prognosis, and is a novel diagnostic and prognostic marker. Fei et al. demonstrated that NKX2-1- AS1 directly targets miR-145-5p Upregulation of SERPINE1 promotes tumour progression and angiogenesis in GC cells [ 27 ] . Kinase insert domain receptor (KDR) activation is associated with the immunosuppressive microenvironment. Concurrently, the presence of KDR mutation was indicative of an immune-hot status, characterised by elevated PD-L1 expression and a high abundance of cytotoxic lymphocytes [ 28 ] . Angiopoietin-like protein 4 (ANGPTL4), as one of the factors regulating tumor angiogenesis, is a multifunctional cytokine involved in angiogenesis, regulation of energy metabolism, and development and metastasis of neoplastic diseases [ 29 ] . Zhou et al. [ 29 ] proposed that in response to hypoxia or HIF-1α stimulation, the expression of ANGPT4 is observed to increase in vascular endothelial cells and tumor cells. This, in turn, has the potential to promote the proliferation, survival, migration, and branching formation of vascular endothelial cells by increasing the phosphorylation of TIMEs. Song et al. concluded that the knockdown of ANGPTLs4 inhibited energy metabolism and proliferation in NSCLC. Although ANGPTLs4 does not significantly affect glycolysis, it affects glutamine consumption and fatty acid oxidation [ 30 ] . Fang et al. propose that ANGPTL4 promotes resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinoma cells, thereby accelerating disease progression [ 31 ] . Combined with the PPI network results, this implies that SERPING1 may be therapeutic through its action on KDR. This study provides a detailed evaluation, encompassing identification and drug-binding characterization, and proposes a drug target for lung cancer with substantial evidence. The study has the potential to improve lung cancer screening methods and provide valuable evidence for the development of screening and prevention strategies, especially for high-risk individuals affected by genetic factors. In further research, we will conduct in vitro experiments to investigate the effects of the identified proteins on LC cell lines, with a view to validating the functional role of the proteins. This includes the measurement of alterations in cell proliferation, migration, and cell secretion. In addition, the therapeutic properties of targeting these proteins can be assessed through in vivo studies using animal models. In order to facilitate the transition to a personalized medicine approach, the following steps are to be taken: Firstly, we will investigate the potential of the identified proteins as reliable biomarkers for lung cancer. This may involve the analysis of patient samples to assess the correlation between protein levels, disease progression, treatment response, and patient prognosis. Secondly, the development of targeted therapies that specifically inhibit or modulate the identified active proteins is to be investigated. In this regard, experiments may be designed to assess the efficacy of inhibitors, monoclonal antibodies, or gene therapy in impeding lung cancer cell growth or enhancing cancer cell sensitivity to existing treatments. In addition, the efficacy and safety of personalized therapies incorporating targeted therapies based on identified proteins will be evaluated through the proposal of clinical trials. The design of these trials will include the stratification of patients based on biomarker expression levels or lung cancer subtypes, with the aim of assessing treatment response in specific subgroups. Our study has several strengths. Firstly, we used MR based on GWAS data and false-positive results screened by reverse Mendelian randomization. Our findings are robust and not affected by horizontal pleiotropy or confounding factors. Our findings are expected to improve gene-based screening programs for lung cancer patients and provide valuable evidence for developing screening and prevention strategies, especially for high-risk individuals affected by confounding genetic factors. As the molecular mechanisms of lung cancer are still unknown, we performed enrichment analyses and constructed a PPI network to reveal regulatory connections and facilitate the development of new therapies. Finally, we used molecular docking technology to assess the binding affinity of the target genes to potential therapeutics, further confirming the druggable value of these targets. This study has several drawbacks: First, it is based on a European database, which may or may not be applicable to other ethnic groups. Second, it lacked data on plasma protein levels from other tissues, which makes it difficult to correlate plasma protein levels from other tissues with lung cancer. In addition, a more comprehensive GWAS database and advanced analytical methods or experimental validation are needed to elucidate the relationship between individual plasma proteins and lung cancer and its mechanisms. Finally, the practical application value of candidate plasma proteins must be validated by comprehensive clinical trials. Future studies could explore the mechanism of targeted therapy for lung cancer. There are also some disadvantages of our study that are worth considering. Firstly, although Mendelian randomization offers a valuable insight into drug use, actual clinical trials are influenced by a variety of factors, so it cannot fully be substituted for the results of clinical trials. Second, the applicability of this study to other racial groups needs to be further investigated, as this study only included Europeans. The reliance on blood pQTl presents a challenge in identifying the most effective tissue for treatment. Focusing only on blood pQTl may not provide a complete picture of the disease and its potential treatments, as it is well known that different tissues in the body may have different gene regulatory mechanisms. Despite rigorous efforts to minimize bias, MR remains to some extent susceptible to unmeasured factors or multiple effects. These limitations and their potential impact on a researcher's conclusions are important to acknowledge. This method of analysis relies on predefined genomes or pathways. However, it should be noted that these predefined elements may not cover all biological mechanisms or interactions. The accuracy of molecular docking depends on the quality of the protein. This approach is an essential step in identifying possible drug targets, but it does not guarantee their efficacy in the clinical setting. Experimental validation and clinical trials are therefore necessary for the determination of the therapeutic potential of a drug target. Conclusion In conclusion, this study confirmed the causal relationship between multiple plasma proteins and lung cancer through comprehensive MR analysis. This provides a new method for understanding biological mechanisms of lung cancer and helps explore early intervention and treatment. Nevertheless, further studies are needed to validate this candidate plasma protein. Declarations Funding This article was not funded. Data availability statement Original contributions from the study are included in the article/supplementary material and further enquiries can be directed to the corresponding author. Ethics statement There was no need to get informed consent or ethical approval for this study again because all of the data were taken from published sources, and the informed consent and approval were received. Author contribution XD S, ZL M is responsible for writing the paper. XD S, ZL M are co-authors. WJ Y is responsible for the design of the article, data collection, and analysis of the results. Acknowledgments We extend our gratitude to the deCODE Genetics Consortium for providing the summary statistics essential for Mendelian randomization analyses. We also acknowledge the invaluable contributions of the researchers who shared these data and all the authors who participated in this study Ethics approval and consent to participate Ethical approval was waived because this study used the data from publicly available databases. Consent for publication Not application. Competing interests The authors declare that they have no competing interests. Clinical Trial Number Clinical trial number: not applicable. Author details 1 Radiology Department, Qingdao Municipal Hospital, Qingdao, Shandong, China; 2 Department of thoracic surgery, The third affiliated hospital of Xinjiang medical university, Urumuqi, China； References Kratzer T B, Bandi P, Freedman N D, et al. Lung cancer statistics, 2023[J]. Cancer, 2024,130(8):1330-1348. Luo G, Zhang Y, Etxeberria J, et al. Projections of Lung Cancer Incidence by 2035 in 40 Countries Worldwide: Population-Based Study[J]. JMIR Public Health Surveill, 2023,9:e43651. Li Y, Yan B, He S. Advances and challenges in the treatment of lung cancer[J]. Biomed Pharmacother, 2023,169:115891. Wu J, Lin Z. Non-Small Cell Lung Cancer Targeted Therapy: Drugs and Mechanisms of Drug Resistance[J]. Int J Mol Sci, 2022,23(23). Zhou K, Li S, Zhao Y, et al. Mechanisms of drug resistance to immune checkpoint inhibitors in non-small cell lung cancer[J]. Front Immunol, 2023,14:1127071. Ying Q, Fan R, Shen Y, et al. Small Cell Lung Cancer-An Update on Chemotherapy Resistance[J]. Curr Treat Options Oncol, 2024,25(8):1112-1123. Zheng X, Song X, Zhu G, et al. Nanomedicine Combats Drug Resistance in Lung Cancer[J]. Adv Mater, 2024,36(3):e2308977. Vikas, Sahu H K, Mehata A K, et al. Dual-receptor-targeted nanomedicines: emerging trends and advances in lung cancer therapeutics[J]. Nanomedicine (Lond), 2022,17(19):1375-1395. Wathoni N, Puluhulawa L E, Joni I M, et al. Monoclonal antibody as a targeting mediator for nanoparticle targeted delivery system for lung cancer[J]. Drug Deliv, 2022,29(1):2959-2970. Hwang S H, Lee S, Park J Y, et al. Potential of Drug Efficacy Evaluation in Lung and Kidney Cancer Models Using Organ-on-a-Chip Technology[J]. Micromachines (Basel), 2021,12(2). Monteduro A G, Rizzato S, Caragnano G, et al. Organs-on-chips technologies - A guide from disease models to opportunities for drug development[J]. Biosens Bioelectron, 2023,231:115271. Li P, Li Y, Ma X, et al. Identification of naphthalimide-derivatives as novel PBD-targeted polo-like kinase 1 inhibitors with efficacy in drug-resistant lung cancer cells[J]. Eur J Med Chem, 2024,271:116416. Ismail M, Davies G, Sproat G, et al. High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110alpha with KRAS[J]. SLAS Discov, 2024,29(8):100197. Santos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets[J]. Nat Rev Drug Discov, 2017,16(1):19-34. Byun J, Han Y, Li Y, et al. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer[J]. Nat Genet, 2022,54(8):1167-1177. Luan Y, Xian D, Zhao C, et al. Therapeutic targets for lung cancer: genome-wide Mendelian randomization and colocalization analyses[J]. Front Pharmacol, 2024,15:1441233. Ferkingstad E, Sulem P, Atlason B A, et al. Large-scale integration of the plasma proteome with genetics and disease[J]. Nat Genet, 2021,53(12):1712-1721. Franzen O, Gan L M, Bjorkegren J. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data[J]. Database (Oxford), 2019,2019. Byrne J, Baker K, Houston A, et al. IL-36 cytokines in inflammatory and malignant diseases: not the new kid on the block anymore[J]. Cell Mol Life Sci, 2021,78(17-18):6215-6227. Murrieta-Coxca J M, Gutierrez-Samudio R N, El-Shorafa H M, et al. Role of IL-36 Cytokines in the Regulation of Angiogenesis Potential of Trophoblast Cells[J]. Int J Mol Sci, 2020,22(1). Kessenbrock K, Plaks V, Werb Z. Matrix metalloproteinases: regulators of the tumor microenvironment[J]. Cell, 2010,141(1):52-67. Pezeshkian Z, Nobili S, Peyravian N, et al. Insights into the Role of Matrix Metalloproteinases in Precancerous Conditions and in Colorectal Cancer[J]. Cancers (Basel), 2021,13(24). Juurikka K, Dufour A, Pehkonen K, et al. MMP8 increases tongue carcinoma cell-cell adhesion and diminishes migration via cleavage of anti-adhesive FXYD5[J]. Oncogenesis, 2021,10(5):44. Palavalli L H, Prickett T D, Wunderlich J R, et al. Analysis of the matrix metalloproteinase family reveals that MMP8 is often mutated in melanoma[J]. Nat Genet, 2009,41(5):518-520. Wu Y, Liu H, Sun Z, et al. The adhesion-GPCR ADGRF5 fuels breast cancer progression by suppressing the MMP8-mediated antitumorigenic effects[J]. Cell Death Dis, 2024,15(6):455. Peng S, Du T, Wu W, et al. Decreased expression of serine protease inhibitor family G1 (SERPING1) in prostate cancer can help distinguish high-risk prostate cancer and predicts malignant progression[J]. Urol Oncol, 2018,36(8):361-366. Teng F, Zhang J X, Chen Y, et al. LncRNA NKX2-1-AS1 promotes tumor progression and angiogenesis via upregulation of SERPINE1 expression and activation of the VEGFR-2 signaling pathway in gastric cancer[J]. Mol Oncol, 2021,15(4):1234-1255. Cui Y, Zhang P, Liang X, et al. Association of KDR mutation with better clinical outcomes in pan-cancer for immune checkpoint inhibitors[J]. Am J Cancer Res, 2022,12(4):1766-1783. Zhou W, Zhang Q, Chen J, et al. Angiopoietin-4 expression and potential mechanisms in carcinogenesis: Current achievements and perspectives[J]. Clin Exp Med, 2024,24(1):224. Xiao S, Nai-Dong W, Jin-Xiang Y, et al. ANGPTL4 regulate glutamine metabolism and fatty acid oxidation in nonsmall cell lung cancer cells[J]. J Cell Mol Med, 2022,26(7):1876-1885. Fang Y, Li X, Cheng H, et al. ANGPTL4 Regulates Lung Adenocarcinoma Pyroptosis and Apoptosis via NLRP3\ASC\Caspase 8 Signaling Pathway to Promote Resistance to Gefitinib[J]. J Oncol, 2022,2022:3623570. Additional Declarations No competing interests reported. Supplementary Files file.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 May, 2025 Reviews received at journal 27 May, 2025 Reviews received at journal 27 May, 2025 Reviews received at journal 23 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 02 May, 2025 Submission checks completed at journal 02 May, 2025 First submitted to journal 23 Apr, 2025 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-6514191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454331317,"identity":"16fed834-0f76-496e-90fb-b7b3f3b65dd6","order_by":0,"name":"Xiao-dong Shao","email":"","orcid":"","institution":"Qingdao Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-dong","middleName":"","lastName":"Shao","suffix":""},{"id":454331319,"identity":"fd8de68e-2be6-432f-8d0a-ac4dbcc5b03f","order_by":1,"name":"Zhou-lin Miao","email":"","orcid":"","institution":"Qingdao Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhou-lin","middleName":"","lastName":"Miao","suffix":""},{"id":454331321,"identity":"d0d250eb-8914-43ae-b777-22ca0a858a64","order_by":2,"name":"Wei-jie Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACeYbDBx//qWCzY2NvIFKLYeOxZAOeM3zJfDwHiLXm8BkzCd42OcZ5EglE6mBsO2AgIXHGjJlN8vHGGww1NtEEtbDzHEgwMKhI42OTTiu2YDiWlttA0JYZBw4kJJw5xswmnWMmwdhwmLAWhvsPGw4cbPvP2CZ5hlgtBw4zNja2sTG2SfAQqcWw4RgzM8MZtmQ2HqBfEojxizzD+e+/GYBRKd9+eOONDzU2RDgMCRgQHTVIWkjVMQpGwSgYBSMDAAD5kz/743LK7wAAAABJRU5ErkJggg==","orcid":"","institution":"Tumor Hospital of Xinjiang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei-jie","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-04-23 15:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6514191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6514191/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82616491,"identity":"a06a3dae-cd16-4bca-b31a-23bed1cca324","added_by":"auto","created_at":"2025-05-13 11:41:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218400,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design overview.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/0c2bd44962abf06b55de006a.png"},{"id":82614955,"identity":"081a4488-b371-4655-8f3f-e1cd0360d79b","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1520632,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization results for human plasma proteins and lung cancer risk. A.Manhattan plot. Genes that were deemed to be significant were labeled and highlighted in red. The blue line indicates the level of statistical significance at the nominal threshold of 0.05. B. Mendelian randomization of risk gene circle maps. The outermost circle denotes the risk gene name. The figure comprises five rings, each representing a distinct method. The color red indicates pval\u0026lt;0.05. C. Volcano plot of the MR analysis for 4907 plasma proteins on lung cancer. D. Forest plots of Mendelian randomization results.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/3d3547927ba55457e698a926.png"},{"id":82614954,"identity":"956df91f-1b3f-4549-9435-643f9b35653d","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":496495,"visible":true,"origin":"","legend":"\u003cp\u003eReverse Mendelian randomization results. A.Forest plots of reverse Mendelian randomization results demonstrate that the integrated effect value of SNPs is less than 0, indicating a protective effect on protein expression with lung cancer development B. The relative symmetry on both sides of the vertical line in the funnel plot indicates no heterogeneity in the results. C. Leave-one-out sensitivity analysis indicates that Mendelian randomization results are relatively stable after removing an SNP. D.The scatter plot shows protective effects on protein expression as lung cancer occurs.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/735864edff67a75c5893bde4.png"},{"id":82614961,"identity":"2fabf7b7-1da6-49e0-a35f-e07cb813d9e4","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":398293,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of results from Mendelian randomization analyses. A.GO enrichment results. B.KEGG enrichment results.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/c5fcd7bc10959d333abbfade.png"},{"id":82617121,"identity":"7c119d40-73bd-44cb-8bfc-da6f23afbdd8","added_by":"auto","created_at":"2025-05-13 11:49:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":378782,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction networks between causal proteins of LUAD. A. PPI network. B. Interaction networks between screened core proteins\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/c4ddc0cebbf409205e4b680d.png"},{"id":82614968,"identity":"03a44053-1c71-4bc5-a03a-1f92b0a7bf7f","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":236051,"visible":true,"origin":"","legend":"\u003cp\u003eGene-Drug enrichment analysis\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/d227cb8cb6f84262bb681671.png"},{"id":82616498,"identity":"69a21967-ce05-42b6-8378-ac41d5cc046e","added_by":"auto","created_at":"2025-05-13 11:41:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":467367,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking. (A) KDR docking with Vandetanib. (B) KDR docking with ANGPTL4.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/7eb136cc9777c1415006f6e5.png"},{"id":82614964,"identity":"57544be3-2994-4cd0-99ac-8189b98ed519","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":298811,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell sequencing results. A. Single-cell sequencing results from lung tissue.B. Distribution of ANGPTL4 in lung tissue. C. Distribution of KDR in lung tissue\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/4ae5d48bc6d97f0926a33417.png"},{"id":82618107,"identity":"71a00345-91c0-47a8-b169-a3eabd00e444","added_by":"auto","created_at":"2025-05-13 11:57:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4852817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/7a7f63ea-8bdc-49d2-83d2-898127ba0cba.pdf"},{"id":82614953,"identity":"acf43b70-1eaa-46da-9966-17dbb0973e46","added_by":"auto","created_at":"2025-05-13 11:33:43","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20066,"visible":true,"origin":"","legend":"","description":"","filename":"file.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6514191/v1/58aabd067cb696025972599a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification of promising lung cancer drug targets from human plasma proteins: Mendelian randomization, single-cell RNA sequencing analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer(LC) is currently the leading malignant tumor in the world and the primary cause of cancer-related deaths\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to expert predictions, lung cancer may become a major public health concern globally by 2035\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Current therapeutic strategies for the treatment of this condition consist of surgical resection, chemotherapy, targeted therapy, and radiotherapy. Nevertheless, despite the range of treatment options available, the prognosis remains poor\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Despite the large amount of research invested in the pathogenesis of lung cancer, the number of valuable genes that have been accessed is still in the minority due to the small size of the studies and their susceptibility to known causal factors. It is imperative to understand the genomic and protein structure of lung cancer in order to comprehend its pathogenesis and to develop personalised, targeted therapies. It is important to note that a significant proportion of lung cancer patients cannot be matched to targeted drugs and that acquired resistance is common after treatment \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In recent years, the combination of novel nanocarriers and targeted therapies has facilitated the development of multimodal targeted therapies for lung cancer \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The advent of high-throughput drug screening and personalized precision medicine, founded upon organ-on-a-chip technology, has concomitantly engendered a multiplicity of opportunities for the treatment of lung cancer\u003csup\u003e[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the overall 5-year survival rate for lung cancer patients remains suboptimal. Consequently, there is an urgent need to identify new therapeutic targets for lung cancer.\u003c/p\u003e \u003cp\u003eProteins function as a critical link between genetics and phenotypes, and complex protein-protein interactions that mediate signaling pathways and biological processes play a significant role in the underlying causes of lung cancer. Simultaneously, whether physically proximal to protein-coding genes (cis-) or located anywhere else in the genome (trans-), these interactions broadly influence protein structure and function, which is of great significance to complex diseases. Proteins circulating in the blood provide fundamental information about human health and can serve as potential biomarkers and drug targets. They are the primary targets of FDA-approved drugs\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Genome-wide association studies (GWAS) of plasma proteins have demonstrated the presence of genetic variants associated with these proteins. Such variants are known as \"protein quantitative trait loci (pQTLs).\" The advent of next-generation proteomics technologies has enabled the identification of unusual protein expression, thus facilitating further exploration of potential biomarkers and cancer therapeutic targets and contributing to our understanding of the underlying mechanisms of the disease. Despite the identification of numerous risk loci associated with lung cancer by GWAS, the genetic basis of this disease is still not fully understood, and GWAS data cannot be directly used for drug development\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Mendelian randomization (MR) analyses same leverage genetic (SNP) data from GWAS databases to infer causal relationships between exposure and outcome variables and discern associations between proteins and phenotypes. These genetic instruments are usually independent of confounders and are not influenced by environmental, behavioral, psychological, or socio-economic influences after birth. Recent studies have identified the utility of MR in the identification of drug targets. This approach has been demonstrated to facilitate the identification of causal effects of plasma proteins on lung cancer while simultaneously reducing experimental bias and confounding factors. Moreover, MR has yielded encouraging results in identifying potential biomarkers and evaluating risk, protective factors, and biological mechanisms associated with lung cancer.\u003c/p\u003e \u003cp\u003eTherefore, the objective of the current study was to identify potential plasma proteins associated with lung cancer. Subsequent analysis of their protein-protein interactions (PPI) and regulatory roles in key cellular pathways enabled the identification of optimal targets for novel interventions. Subsequently, enrichment analysis was employed to ascertain the intricate pathogenesis of these lung cancer-associated plasma proteins. In order to ensure the robustness of the results, a sensitivity analysis and a reverse MR were performed. The integration of MR, drug prediction, gene enrichment analysis, and the construction of protein-protein interaction (PPI) networks are expected to provide valuable guidance for developing more effective and targeted therapeutic approaches and new directions for discovering new therapeutic targets for lung cancer. Through single-cell RNA sequencing analysis, it is expected that the potential causal role of specific plasma proteins in the pathogenesis of lung cancer will be illustrated.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThe association between plasma proteins and LC was explored via MR, leveraging pooled data from 4907 proteomics studies\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the overall schematic of this study. In addition, analysis of filtered proteins was used to assess druggability and prioritize therapeutic targets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExposure data\u003c/h2\u003e \u003cp\u003eWe obtained human 4907 plasma proteins from a database (deCODE genetics | a global leader in human genetics) and conducted screening procedures\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. All 4907 protein data were derived from 35,559 Icelandic individuals. Exposure data were subject to the following conditions: (a) SNP selection. SNP including both cis - SNPs and trans - SNPs with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8 ,were selected; (b) Independent Associations. To ensure independent associations, linkage disequilibrium (LD) aggregation r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and genetic distance was set to 10,000 kb, which was required. This investigation was guided by three key principles: (1) the relevance criterion: The IVs must exhibit notable associations with the exposure variables. This is crucial as it validates the use of IVs to represent the exposure in the analysis; (2) the independence criterion: The IVs should remain independent of any potential confounders, whether they are known or unknown; (3) the exclusion restriction criterion: The IVs exert their influence on the outcome exclusively through the exposure entities. The application of this principle facilitates the identification of the causal pathway from the exposure to the outcome via the instrumental variables (IVs). In order to avoid weak instrumental variable bias, genetic variables with an \u003cem\u003eF\u003c/em\u003e statistic of less than 10 were excluded.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAccess to outcome data\u003c/h3\u003e\n\u003cp\u003eFor the main results, we found summary genetic association data for lung cancer from the IEU Open GWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We employed the search term \u0026lsquo;lung cancer\u0026rsquo; to explore datasets. Through the application of inclusion and exclusion criteria, we selected studies that met our research objectives. Ultimately, we obtained a dataset with the ID: ebi-a-GCST90018875. This dataset consisted of individuals of European ancestry, comprising 3791 lung cancer cases and 489012 controls.\u003c/p\u003e\n\u003ch3\u003eMendelian Randomization analysis\u003c/h3\u003e\n\u003cp\u003eWe performed MR analysis based on cis and trans pQTL from deCODE. Firstly, the exposure and outcome cohorts were predominantly composed of individuals of European ancestry, thereby minimizing potential biases arising from population distribution disparities. By employing the \u0026ldquo;TwoSampleMR\u0026rdquo; R package in R version 4.4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MRCIEU/TwoSampleMR\u003c/span\u003e\u003cspan address=\"https://github.com/MRCIEU/TwoSampleMR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we designated plasma proteins as the exposure variable and lung cancer as the outcome variable to execute the primary Mendelian Randomization (MR) analysis.\u003c/p\u003e \u003cp\u003eWe utilized five distinct methods to estimate the causal effects: MR - IVW (Inverse Variance Weighting), MR - Egger, weighted median, simple mode, and weighted mode. Among these, the principal method for establishing causation was inverse variance weighting (IVW). Using multiple methods enhanced the robustness and comprehensiveness of our causal inference, with IVW serving as the cornerstone in determining the most reliable estimates of the causal relationships between plasma proteins and lung cancer. This approach allowed us to systematically explore and quantify the potential causal links, providing a more in-depth understanding of the underlying biological mechanisms and potential implications for disease prevention and treatment strategies.\u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eThe potential presence of pleiotropy was further explored using the MR-Egger method to assess its potential impact. In cases where the MR-Egger intercept value was less than 0.05, we classified these protein-disease associations as being influenced by horizontal pleiotropy. Additionally, we employed Cochran's test to evaluate the potential heterogeneity of MR estimates derived from both the MR-Egger and IVW methods (with a significance level of value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Proteins exhibiting horizontal pleiotropy and heterogeneity were excluded from all subsequent follow-up analyses. This exclusion was crucial to ensure the integrity and reliability of our results, as pleiotropy and heterogeneity can introduce confounding factors and undermine the accuracy of causal inference. To validate the absence of reverse causality, we implemented reverse Mendelian randomization for the disease-associated proteins identified in the screening process. We set the thresholds \u003cem\u003ep\u003c/em\u003e1 and \u003cem\u003ep\u003c/em\u003e2 to be lower than 5 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and considered the p-value, the result of IVW, as the primary metric for observation. The results of this analysis were then visualized to provide a clear and intuitive understanding of the relationships between the variables. This step was essential in strengthening the causal interpretation of our findings and ensuring that the observed associations were not due to reverse causation, thereby enhancing our study's overall validity and credibility.\u003c/p\u003e\n\u003ch3\u003eDisease-associated protein filtering\u003c/h3\u003e\n\u003cp\u003eThe protein filtering method must satisfy the following conditions: 1. The p-value of IVW\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 2. The OR of the five Mendelian randomization calculations are all simultaneously greater than one or less than 1; and 3. The pval of pleiotropy is more significant than 0.05.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein - Protein Network Analysis\u003c/h2\u003e \u003cp\u003eIn order to delve into the interactions between potential therapeutic targets and the underlying mechanisms of LC, a Protein - Protein Interaction (PPI) analysis was conducted. The PPI analysis was used through the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Cytoscape software, with a minimum required interaction score of 0.4. Sort the proteins by the number of connected nodes and take the top ten. The top ten proteins with the highest number of connected nodes were selected for the next step in order to explore druggability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCandidate Drug Prediction\u003c/h3\u003e\n\u003cp\u003eTo evaluate the druggability of the identified target proteins, we initiated the process by predicting potential candidate drugs using the Drug Signatures Database (Dsigdb, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dsigdb.tanlab.org/\u003c/span\u003e\u003cspan address=\"https://dsigdb.tanlab.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Dsigdb is a comprehensive database linking medications and other chemical compounds to their target genes. This resource provides a valuable platform for exploring the relationships between drugs and genes, enabling us to identify potential drugs that can interact with the target proteins of interest. By leveraging the information in Dsigdb, we can narrow down the list of candidate drugs and prioritize them for further investigation, which is crucial for developing effective therapeutic strategies. Gene-drug pathway enrichment was performed using the Enrichers platform. Drug structure data were obtained from the PubChem Compound Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the corresponding IDs are shown. Protein structure data were obtained from the PDB (Protein Data Bank, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, a novel blind docking tool named CB - Dock2 was employed. The primary objective of CB - Dock2 is to enhance the accuracy of docking procedures. It achieves this by predicting the binding region of a given protein. The ligand molecule can bind spontaneously to the receptor protein if its binding energy is negative. A lower binding energy is indicative of a more tightly binding interaction between the two molecules.\u003c/p\u003e\n\u003ch3\u003eSingle-cell RNA sequencing analysis\u003c/h3\u003e\n\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) data for lung tissue was obtained from the Panglao DB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panglaodb.se/\u003c/span\u003e\u003cspan address=\"https://panglaodb.se/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This database is designed to meet the needs of the scientific community, with a focus on single-cell RNA sequencing experiments in mice and humans\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The dataset was then searched using the 'Samples' module. Filter criteria were set to 'Human and mouse' and 'Tissue.' The selection was based on the dataset that contained the most cell types.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eIdentified 46 plasma proteins associated with LC by MR\u003c/h2\u003e\n \u003cp\u003eIn the present study, data from GWAS and deCODE were employed. Through MR analysis, following the exclusion of proteins exhibiting pleiotropy, 46 plasma proteins were successfully identified as being causally associated with lung cancer(Supplementary Table S1). PHGDH(OR = 1.01, 95%CI:1.00-1.20, p = 0.049)、MMP19(OR = 1.13, 95%CI:1.00-1.23, p = 0.049)、SMPDL3A(OR = 1.12, 95%CI:1.00-1.24, p = 0.045)、PAPPA(OR = 1.01, 95%CI:1.00-1.20, p = 0.04)、SLC5A8(OR = 1.09, 95%CI:1.00-1.18, p = 0.040)、SCARB2(OR = 1.49, 95%CI:1.001, p = 0.047)、BIRC7(OR = 1.15, 95%CI:1.01–1.30, p = 0.040)、LGALS3BP(OR = 1.10, 95%CI:1.01–1.20, p = 0.036)、ANGPTL4(OR = 1.13, 95%CI:1.01–1.27, p = 0.038)、SEMA4B(OR = 1.30, 95%CI:1.01–1.67, p = 0.042)、TNS4(OR = 1.18, 95%CI:1.01–1.39, p = 0.038)、GRAMD1C(OR = 1.12, 95%CI:1.01–1.24, p = 0.032)、CPOX(OR = 1.14, 95%CI:1.01–1.28, p = 0.032)、TCTN2(OR = 1.15, 95%CI:1.01–1.31, p = 0.032)、TMCC3(OR = 1.11, 95%CI:1.01–1.23, p = 0.027)、IFIT2(OR = 1.24, 95%CI:1.01–1.51, p = 0.038)、SECTM1(OR = 1.14, 95%CI:1.01–1.28, p = 0.028)、CD33(OR = 1.08, 95%CI:1.015–1.15, p = 0.016)、WFIKKN2(OR = 1.01, 95%CI:1.017–1.19, p = 0.017)、APOF(OR = 1.19, 95%CI:1.02–1.40, p = 0.030)、CLN5(OR = 1.25, 95%CI:1.02–1.54, p = 0.031)、NTF3(OR = 1.31, 95%CI:1.024–1.68, p = 0.032)、FAIM(OR = 1.19, 95%CI:1.03–1.31, p = 0.018)、B3GALT1(OR = 1.54, 95%CI:1.03–2.30, p = 0.036)、TMEM70(OR = 1.28, 95%CI:1.04–1.58, p = 0.022)、DGCR2(OR = 1.57, 95%CI:1.04–2.39, p = 0.033)、IL36A(OR = 1.20, 95%CI:1.041–1.38, p = 0.012)、LRRC24(OR = 1.47, 95%CI:1.046–2.077, p = 0.027)、CCNA2(OR = 1.43, 95%CI:1.01–1.93, p = 0.019)、HSD17B11(OR = 1.52, 95%CI:1.066–2.17, p = 0.021)、POLR3F(OR = 1.56, 95%CI:1.07–2.27, p = 0.020)were associated with an increased risk of LC. Notably, IL36A exhibited the most significant MR result. In contrast, RIC3(OR = 0.042, 95%CI:0.55–0.99, p = 0.042)、DEFB135(OR = 0.76, 95%CI:0.61–0.96, p = 0.019)、CLEC4M(OR = 0.77, 95%CI:0.61–0.98, p = 0.031)、COL2A1(OR = 0.78, 95%CI:0.64–0.95, p = 0.016)、BLVRB(OR =, 95%CI:, p = 0.039)、DEFA1(OR = 0.82, 95%CI:0.67–0.99, p = 0.042)、JPH1(OR = 0.82, 95%CI:0.67-1.00, p = 0.048)、MMP8(OR = 0.87, 95%CI:0.78–0.97, p = 0.013)、SERPING1(OR = 0.89, 95%CI:0.81–0.98, p = 0.022)、KIR2DL3(OR = 0.90, 95%CI:0.82–0.99, p = 0.025)、PLTP(OR = 0.92, 95%CI:0.84-1.00, p = 0.049)、ACP1(OR = 0.92, 95%CI:0.84–0.99, p = 0.036)、KDR(OR = 0.92, 95%CI:0.85-1.00, p = 0.048)、CLPS(OR = 0.92, 95%CI:0.86-1.00, p = 0.037)、GNLY(OR = 0.93, 95%CI:0.87-1.00, p = 0.041) were associated with an decreased risk of LC. Among them, MMP8 showed the most significant protective effect (Fig.\u0026nbsp;2A-D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eReverse Mendelian randomisation results\u003c/h2\u003e\n \u003cp\u003eTo further explore issue of reverse causality, we subsequently conducted a MR analysis for the 46 potential plasma proteins identified in the primary analysis. However, we found that for the IVW method, the p - value was less than 0.05 between COL2A1, MMP19 and LC. This indicates a reciprocal causal relationship between lung cancer and two proteins. Simultaneously, the incidence of lung cancer has a negative impact on the expression of COL2A2 and MMP19. Therefore, further visualization of the results of the reverse Mendelian randomization is necessary (Fig.\u0026nbsp;3A-D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eExploring the Biological Significance by Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eEnrichment analysis, specifically through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, provides a powerful approach to comprehensively understanding the biological functions, metabolic pathways, and interactions of similarly expressed proteins.\u003c/p\u003e\n \u003cp\u003eThe GO annotation results for the top 10 proteins revealed notable enrichments. In the Biological Process (BP) category, lipid transport was the central process that was significantly enriched. Regarding the Cellular Component (CC), the collagen-containing extracellular matrix was the prominent feature. For the Molecular Function (MF) of these top 10 proteins, lipid transporter activity, cholesterol binding, and sterol binding were the main molecular functions involved.\u003c/p\u003e\n \u003cp\u003eUpon conducting the KEGG enrichment analysis, it was discovered that these proteins were predominantly associated with several key pathways. These included Porphyrin metabolism, Cholesterol metabolism, and the PPAR signaling pathway. These findings suggest that these proteins play crucial roles in lipid-related biological processes and are involved in specific metabolic and signaling pathways, which may have important implications for understanding the underlying mechanisms of the biological phenomena under investigation. This knowledge can further guide future research to explore the detailed molecular mechanisms and potential therapeutic targets related to these pathways and functions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eConstruction of protein-protein interaction networks\u003c/h2\u003e\n \u003cp\u003eTo enhance our comprehension of the potential biological relationships among these proteins and their functions that impact lung cancer, a protein-protein interaction (PPI) network was constructed for the important proteins that were identified through the process of Mendelian randomization (Fig.\u0026nbsp;5A). Importing the PPI network data into Cytoscape, we identified 10 proteins (SERPING1, LGALS3BP, IFIT2, PHGDH, PLTP, KDR, SCARB2, ANGPTL4, MMP8, CD33) as core proteins(Fig.\u0026nbsp;5B). Figure B shows that KDR and SERPING1 are the key core proteins. In the figure, the greater the number of protein interactions a protein has, the heavier its color appears.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003ePrediction of Candidate Drug\u003c/h2\u003e\n \u003cp\u003eThis study used the DSigDB database to predict potentially effective interventional drugs. The drugs found to be the most promising targets were KDR, MMP8,, ANGPTL4, PLTP, SCARB2, and PHGDH. Idocyklin was the most relevant and meaningful drug related to KDR and MMP8. The top 9 potential compounds are shown based on the count value (Table\u0026nbsp;1). Drug-associated genes were MMP8, ANGPTL4, PLTP, and KDR. The top 9 significantly enriched drugs were Idocyklin, Deguelin, Vandetanib, Hydrogen, 1-phosphatidyl-myo-inositol, SARIN, ascorbic acid, and Dinoprostone.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCandidate Drug Predicted Using DSigDB database\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdocyklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdocyklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR/MMP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeguelin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeguelin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR/ANGPTL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVandetanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVandetanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR/ANGPTL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR/MMP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Phosphatidyl-myo-inositol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-Phosphatidyl-myo-inositol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLTP/KDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLTP/SCARB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eascorbic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eascorbic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHGDH/KDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDinoprostone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDinoprostone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR/MMP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eMolecular Docking\u003c/h2\u003e\n \u003cp\u003eCB-dock2 is applied to obtain binding sites and interactions of drug candidates with proteins encoded by the respective genes, and to generate binding energies for each interaction. It is widely accepted that lower binding energies are indicative of enhanced stability. This study performed molecular docking to assess the binding affinity of drug candidates for their targets and, from this, to understand the drug target’s druggability. Unfortunately MMP8, PLTP were not available, so they were failed to predict at this stage. Molecular docking was likewise not possible for vinascore of Hydrogen was \u0026gt; 0, the extensive data for Dinoprostone, the unavailability of structural data for SARIN, and the problematic calculations of 1-phosphatidyl-myo-inositol because of structural issues. Notably, KDR and ANGPTL4 showed significant and stable binding to the drug candidates, suggesting a strong affinity and providing compelling evidence for their druggability. It was decided to molecularly dock and visualize the two proteins separately(Fig.\u0026nbsp;7A-B).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePubChem ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDB ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVinascore\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVandetanib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e3081361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5EW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANGPTL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6EUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eascorbic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e54670067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHGDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7DKM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5EW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIdocyklin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54671203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5EW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDeguelin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e107935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5EW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANGPTL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6EUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e−7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eSingle-cell RNA sequencing analysis results\u003c/h2\u003e\n \u003cp\u003eThe identification code for these samples is SRA653146. Single-cell RNA-sequencing (scRNA-seq) technology offers a means of obtaining cell-specific genetic information, thereby facilitating the elucidation of the detailed functions and roles of target cells. The present study analyzed the scRNA-seq data of lung sample SRA653146 in the Panglao DB database in association with cell clustering results and cell type information. Following rigorous quality control procedures on the data, the t-distributed Stochastic Neighbor Embedding (t-SNE) technique was employed to visualize the single-cell RNA sequencing data. The data downloaded from the Panglao DB database demonstrated that the KDR gene was predominantly expressed in endothelial cells and alveolar type II cells within lung tissues. ANGPTL4 was mainly expressed in fibroblasts, macrophages, and endothelial cells. Therefore, KDR and ANGPTL4 may play an important role in the development of lung cancer.(Fig.\u0026nbsp;8A-C)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe human protein composition is a key area of research that demands significant attention in the pursuit of identifying new drug targets for lung cancer treatment. Using the largest GWAS data, this study investigated the possible causal relationship between plasma proteins and lung cancer risk by Mendelian randomization. It explored potential therapeutic targets for lung cancer in plasma proteins and the molecular mechanisms of lung cancer pathogenesis.\u003c/p\u003e \u003cp\u003eThe IL-36 family of cytokines was first identified in 2000, and IL36A is one of the IL-36 family agonists\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In the last twenty years, little has been known about its role in cancer-related diseases. IL-36A is associated with abnormal angiogenesis, which is critical in tumors and key to disease progression and metastasis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, the exact mechanism needs to be further investigated.\u003c/p\u003e \u003cp\u003eMatrix metalloproteinases (MMP) are zinc-dependent endopeptidases strongly implicated in tumourigenesis by promoting extracellular matrix renewal, cancer cell migration, cell growth, inflammation, angiogenesis, and TME remodeling\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Interestingly, MMP8 is one of the unique members with anti-tumor and anti-metastatic functions. High MMP8 expression is associated with significantly reducing cancer incidence and risk of metastasis and prolonging overall patient survival\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In breast and skin cancers, MMP8 plays an inhibitory role\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This is consistent with our findings. But the internal mechanism in lung cancer needs to be further defined.\u003c/p\u003e \u003cp\u003eIn reverse Mendelian randomization, two proteins including COL2A1、MMP19, and lung cancer were unexpectedly found to be causally related. This has not been reported to elaborate on the intrinsic reason. By the reverse Mendelian randomization study, although false-positive results were found, the possibility of future druggability of other proteins is greatly improved, and the success rate of clinical trials is increased.\u003c/p\u003e \u003cp\u003eEnrichment analyses illustrate the potential pathways and functions involved in the screened risk proteins. Next, 10 core genes (SERPING1, LGALS3BP, IFIT2, PHGDH, PLTP, KDR, SCARB2, ANGPTL4, MMP8, CD33) that may be causally related to lung cancer were identified by plotting the PPI network. Among the 10 core genes, SERPING1 and KDR involve the most proteins. However, the functions of SERPING1 and KDR are not fully understood so far. The PPI analysis showed that SERPING1 interacts with KDR. Experts previously found SERPING1 (serine protease inhibitor family G1 ) to be associated with anomalous angioedema, and less studied in oncological diseases. Some experts and researchers have found that SERPING1 is highly expressed in breast cancer patients, is associated with subsequent bone metastasis, and is considered to be a core gene for breast cancer bone metastasis. Peng et al\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. suggested that low expression of SERPING1 implies a higher degree of malignancy of prostate lesions, is associated with poorer prognosis, and is a novel diagnostic and prognostic marker. Fei et al. demonstrated that NKX2-1- AS1 directly targets miR-145-5p Upregulation of SERPINE1 promotes tumour progression and angiogenesis in GC cells\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Kinase insert domain receptor (KDR) activation is associated with the immunosuppressive microenvironment. Concurrently, the presence of KDR mutation was indicative of an immune-hot status, characterised by elevated PD-L1 expression and a high abundance of cytotoxic lymphocytes\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAngiopoietin-like protein 4 (ANGPTL4), as one of the factors regulating tumor angiogenesis, is a multifunctional cytokine involved in angiogenesis, regulation of energy metabolism, and development and metastasis of neoplastic diseases\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Zhou et al. \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e proposed that in response to hypoxia or HIF-1α stimulation, the expression of ANGPT4 is observed to increase in vascular endothelial cells and tumor cells. This, in turn, has the potential to promote the proliferation, survival, migration, and branching formation of vascular endothelial cells by increasing the phosphorylation of TIMEs. Song et al. concluded that the knockdown of ANGPTLs4 inhibited energy metabolism and proliferation in NSCLC. Although ANGPTLs4 does not significantly affect glycolysis, it affects glutamine consumption and fatty acid oxidation\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Fang et al. propose that ANGPTL4 promotes resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinoma cells, thereby accelerating disease progression\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Combined with the PPI network results, this implies that SERPING1 may be therapeutic through its action on KDR. This study provides a detailed evaluation, encompassing identification and drug-binding characterization, and proposes a drug target for lung cancer with substantial evidence. The study has the potential to improve lung cancer screening methods and provide valuable evidence for the development of screening and prevention strategies, especially for high-risk individuals affected by genetic factors. In further research, we will conduct in vitro experiments to investigate the effects of the identified proteins on LC cell lines, with a view to validating the functional role of the proteins. This includes the measurement of alterations in cell proliferation, migration, and cell secretion. In addition, the therapeutic properties of targeting these proteins can be assessed through in vivo studies using animal models.\u003c/p\u003e \u003cp\u003eIn order to facilitate the transition to a personalized medicine approach, the following steps are to be taken: Firstly, we will investigate the potential of the identified proteins as reliable biomarkers for lung cancer. This may involve the analysis of patient samples to assess the correlation between protein levels, disease progression, treatment response, and patient prognosis. Secondly, the development of targeted therapies that specifically inhibit or modulate the identified active proteins is to be investigated. In this regard, experiments may be designed to assess the efficacy of inhibitors, monoclonal antibodies, or gene therapy in impeding lung cancer cell growth or enhancing cancer cell sensitivity to existing treatments. In addition, the efficacy and safety of personalized therapies incorporating targeted therapies based on identified proteins will be evaluated through the proposal of clinical trials. The design of these trials will include the stratification of patients based on biomarker expression levels or lung cancer subtypes, with the aim of assessing treatment response in specific subgroups.\u003c/p\u003e \u003cp\u003eOur study has several strengths. Firstly, we used MR based on GWAS data and false-positive results screened by reverse Mendelian randomization. Our findings are robust and not affected by horizontal pleiotropy or confounding factors. Our findings are expected to improve gene-based screening programs for lung cancer patients and provide valuable evidence for developing screening and prevention strategies, especially for high-risk individuals affected by confounding genetic factors. As the molecular mechanisms of lung cancer are still unknown, we performed enrichment analyses and constructed a PPI network to reveal regulatory connections and facilitate the development of new therapies. Finally, we used molecular docking technology to assess the binding affinity of the target genes to potential therapeutics, further confirming the druggable value of these targets. This study has several drawbacks: First, it is based on a European database, which may or may not be applicable to other ethnic groups. Second, it lacked data on plasma protein levels from other tissues, which makes it difficult to correlate plasma protein levels from other tissues with lung cancer. In addition, a more comprehensive GWAS database and advanced analytical methods or experimental validation are needed to elucidate the relationship between individual plasma proteins and lung cancer and its mechanisms. Finally, the practical application value of candidate plasma proteins must be validated by comprehensive clinical trials. Future studies could explore the mechanism of targeted therapy for lung cancer.\u003c/p\u003e \u003cp\u003eThere are also some disadvantages of our study that are worth considering. Firstly, although Mendelian randomization offers a valuable insight into drug use, actual clinical trials are influenced by a variety of factors, so it cannot fully be substituted for the results of clinical trials. Second, the applicability of this study to other racial groups needs to be further investigated, as this study only included Europeans. The reliance on blood pQTl presents a challenge in identifying the most effective tissue for treatment. Focusing only on blood pQTl may not provide a complete picture of the disease and its potential treatments, as it is well known that different tissues in the body may have different gene regulatory mechanisms. Despite rigorous efforts to minimize bias, MR remains to some extent susceptible to unmeasured factors or multiple effects. These limitations and their potential impact on a researcher's conclusions are important to acknowledge. This method of analysis relies on predefined genomes or pathways. However, it should be noted that these predefined elements may not cover all biological mechanisms or interactions. The accuracy of molecular docking depends on the quality of the protein. This approach is an essential step in identifying possible drug targets, but it does not guarantee their efficacy in the clinical setting. Experimental validation and clinical trials are therefore necessary for the determination of the therapeutic potential of a drug target.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study confirmed the causal relationship between multiple plasma proteins and lung cancer through comprehensive MR analysis. This provides a new method for understanding biological mechanisms of lung cancer and helps explore early intervention and treatment. Nevertheless, further studies are needed to validate this candidate plasma protein.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was not funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal contributions from the study are included in the article/supplementary material and further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no need to get informed consent or ethical approval for this study again because all of the data were taken from published sources, and the informed consent and approval were received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXD S, ZL M is responsible for writing the paper. XD S, ZL M are co-authors. WJ Y is responsible for the design of the article, data collection, and analysis of the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the deCODE Genetics Consortium for providing the summary statistics essential for Mendelian randomization analyses. We also acknowledge the invaluable contributions of the researchers who shared these data and all the authors who participated in this study\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was waived because this study used the data from publicly available databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eRadiology Department, Qingdao Municipal Hospital, Qingdao, Shandong, China; \u003csup\u003e2\u003c/sup\u003eDepartment of thoracic surgery, The third affiliated hospital of Xinjiang medical university, Urumuqi, China；\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKratzer T B, Bandi P, Freedman N D, et al. Lung cancer statistics, 2023[J]. Cancer, 2024,130(8):1330-1348.\u003c/li\u003e\n\u003cli\u003eLuo G, Zhang Y, Etxeberria J, et al. Projections of Lung Cancer Incidence by 2035 in 40 Countries Worldwide: Population-Based Study[J]. JMIR Public Health Surveill, 2023,9:e43651.\u003c/li\u003e\n\u003cli\u003eLi Y, Yan B, He S. Advances and challenges in the treatment of lung cancer[J]. Biomed Pharmacother, 2023,169:115891.\u003c/li\u003e\n\u003cli\u003eWu J, Lin Z. Non-Small Cell Lung Cancer Targeted Therapy: Drugs and Mechanisms of Drug Resistance[J]. Int J Mol Sci, 2022,23(23).\u003c/li\u003e\n\u003cli\u003eZhou K, Li S, Zhao Y, et al. Mechanisms of drug resistance to immune checkpoint inhibitors in non-small cell lung cancer[J]. Front Immunol, 2023,14:1127071.\u003c/li\u003e\n\u003cli\u003eYing Q, Fan R, Shen Y, et al. Small Cell Lung Cancer-An Update on Chemotherapy Resistance[J]. Curr Treat Options Oncol, 2024,25(8):1112-1123.\u003c/li\u003e\n\u003cli\u003eZheng X, Song X, Zhu G, et al. Nanomedicine Combats Drug Resistance in Lung Cancer[J]. Adv Mater, 2024,36(3):e2308977.\u003c/li\u003e\n\u003cli\u003eVikas, Sahu H K, Mehata A K, et al. Dual-receptor-targeted nanomedicines: emerging trends and advances in lung cancer therapeutics[J]. Nanomedicine (Lond), 2022,17(19):1375-1395.\u003c/li\u003e\n\u003cli\u003eWathoni N, Puluhulawa L E, Joni I M, et al. Monoclonal antibody as a targeting mediator for nanoparticle targeted delivery system for lung cancer[J]. Drug Deliv, 2022,29(1):2959-2970.\u003c/li\u003e\n\u003cli\u003eHwang S H, Lee S, Park J Y, et al. Potential of Drug Efficacy Evaluation in Lung and Kidney Cancer Models Using Organ-on-a-Chip Technology[J]. Micromachines (Basel), 2021,12(2).\u003c/li\u003e\n\u003cli\u003eMonteduro A G, Rizzato S, Caragnano G, et al. Organs-on-chips technologies - A guide from disease models to opportunities for drug development[J]. Biosens Bioelectron, 2023,231:115271.\u003c/li\u003e\n\u003cli\u003eLi P, Li Y, Ma X, et al. Identification of naphthalimide-derivatives as novel PBD-targeted polo-like kinase 1 inhibitors with efficacy in drug-resistant lung cancer cells[J]. Eur J Med Chem, 2024,271:116416.\u003c/li\u003e\n\u003cli\u003eIsmail M, Davies G, Sproat G, et al. High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110alpha with KRAS[J]. SLAS Discov, 2024,29(8):100197.\u003c/li\u003e\n\u003cli\u003eSantos R, Ursu O, Gaulton A, et al. A comprehensive map of molecular drug targets[J]. Nat Rev Drug Discov, 2017,16(1):19-34.\u003c/li\u003e\n\u003cli\u003eByun J, Han Y, Li Y, et al. Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer[J]. Nat Genet, 2022,54(8):1167-1177.\u003c/li\u003e\n\u003cli\u003eLuan Y, Xian D, Zhao C, et al. Therapeutic targets for lung cancer: genome-wide Mendelian randomization and colocalization analyses[J]. Front Pharmacol, 2024,15:1441233.\u003c/li\u003e\n\u003cli\u003eFerkingstad E, Sulem P, Atlason B A, et al. Large-scale integration of the plasma proteome with genetics and disease[J]. Nat Genet, 2021,53(12):1712-1721.\u003c/li\u003e\n\u003cli\u003eFranzen O, Gan L M, Bjorkegren J. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data[J]. Database (Oxford), 2019,2019.\u003c/li\u003e\n\u003cli\u003eByrne J, Baker K, Houston A, et al. IL-36 cytokines in inflammatory and malignant diseases: not the new kid on the block anymore[J]. Cell Mol Life Sci, 2021,78(17-18):6215-6227.\u003c/li\u003e\n\u003cli\u003eMurrieta-Coxca J M, Gutierrez-Samudio R N, El-Shorafa H M, et al. Role of IL-36 Cytokines in the Regulation of Angiogenesis Potential of Trophoblast Cells[J]. Int J Mol Sci, 2020,22(1).\u003c/li\u003e\n\u003cli\u003eKessenbrock K, Plaks V, Werb Z. Matrix metalloproteinases: regulators of the tumor microenvironment[J]. Cell, 2010,141(1):52-67.\u003c/li\u003e\n\u003cli\u003ePezeshkian Z, Nobili S, Peyravian N, et al. Insights into the Role of Matrix Metalloproteinases in Precancerous Conditions and in Colorectal Cancer[J]. Cancers (Basel), 2021,13(24).\u003c/li\u003e\n\u003cli\u003eJuurikka K, Dufour A, Pehkonen K, et al. MMP8 increases tongue carcinoma cell-cell adhesion and diminishes migration via cleavage of anti-adhesive FXYD5[J]. Oncogenesis, 2021,10(5):44.\u003c/li\u003e\n\u003cli\u003ePalavalli L H, Prickett T D, Wunderlich J R, et al. Analysis of the matrix metalloproteinase family reveals that MMP8 is often mutated in melanoma[J]. Nat Genet, 2009,41(5):518-520.\u003c/li\u003e\n\u003cli\u003eWu Y, Liu H, Sun Z, et al. The adhesion-GPCR ADGRF5 fuels breast cancer progression by suppressing the MMP8-mediated antitumorigenic effects[J]. Cell Death Dis, 2024,15(6):455.\u003c/li\u003e\n\u003cli\u003ePeng S, Du T, Wu W, et al. Decreased expression of serine protease inhibitor family G1 (SERPING1) in prostate cancer can help distinguish high-risk prostate cancer and predicts malignant progression[J]. Urol Oncol, 2018,36(8):361-366.\u003c/li\u003e\n\u003cli\u003eTeng F, Zhang J X, Chen Y, et al. LncRNA NKX2-1-AS1 promotes tumor progression and angiogenesis via upregulation of SERPINE1 expression and activation of the VEGFR-2 signaling pathway in gastric cancer[J]. Mol Oncol, 2021,15(4):1234-1255.\u003c/li\u003e\n\u003cli\u003eCui Y, Zhang P, Liang X, et al. Association of KDR mutation with better clinical outcomes in pan-cancer for immune checkpoint inhibitors[J]. Am J Cancer Res, 2022,12(4):1766-1783.\u003c/li\u003e\n\u003cli\u003eZhou W, Zhang Q, Chen J, et al. Angiopoietin-4 expression and potential mechanisms in carcinogenesis: Current achievements and perspectives[J]. Clin Exp Med, 2024,24(1):224.\u003c/li\u003e\n\u003cli\u003eXiao S, Nai-Dong W, Jin-Xiang Y, et al. ANGPTL4 regulate glutamine metabolism and fatty acid oxidation in nonsmall cell lung cancer cells[J]. J Cell Mol Med, 2022,26(7):1876-1885.\u003c/li\u003e\n\u003cli\u003eFang Y, Li X, Cheng H, et al. ANGPTL4 Regulates Lung Adenocarcinoma Pyroptosis and Apoptosis via NLRP3\\ASC\\Caspase 8 Signaling Pathway to Promote Resistance to Gefitinib[J]. J Oncol, 2022,2022:3623570.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lung cancer, mendelian randomization, drug target, Plasma protein","lastPublishedDoi":"10.21203/rs.3.rs-6514191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6514191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Lung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e: Protein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: We identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8（OR = 0.87，95%CI:0.78-0.97，p=0.013）had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A（OR =1.20，95%CI:1.041-1.38，p=0.012) exhibited the most significant MR result . Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. In addition, the researchers used the DSigDB database to predict potentially effective intervening drugs and obtained nine possible drugs. Molecular docking showed that the drugs bind very much to the proteins . KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs.\u003c/p\u003e","manuscriptTitle":"Identification of promising lung cancer drug targets from human plasma proteins: Mendelian randomization, single-cell RNA sequencing analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 11:33:38","doi":"10.21203/rs.3.rs-6514191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-30T04:35:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T14:52:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T13:40:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T14:22:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69776848528280140356469336267693798888","date":"2025-05-22T03:36:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27895043136670929637650352166465821395","date":"2025-05-19T12:14:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186968348695654964483039152477124624169","date":"2025-05-18T09:36:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75897951162071582080672005176621724232","date":"2025-05-13T08:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-09T04:56:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-03T02:44:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-03T02:44:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-23T15:50:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76f2d1ee-f0a8-4930-a28f-9544bc04712c","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-25T06:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 11:33:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6514191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6514191","identity":"rs-6514191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}