Tumor-originated exosomal TREML1 is a novel predictive biomarker for tumorigenesis in lung cancer

Tumor-originated exosomal TREML1 is a novel predictive biomarker for tumorigenesis in lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tumor-originated exosomal TREML1 is a novel predictive biomarker for tumorigenesis in lung cancer Wenliang Qiao, Juan Chen, Yongfeng Yang, Wang Hou, Kaixin Lei, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4616157/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lung cancer is a major contributor to cancer rates and deaths worldwide. Due to its complexity and variability, lung cancer progresses quickly and has a grim outlook, making early and precise diagnosis imperative. Despite numerous clinical methods available to aid doctors in detecting lung cancer, there is still a need for a non-invasive biomarker for cancer development. Methods We examine the levels of TREML1 mRNA and protein expression in exosomes derived from tumors in both normal and cancerous lung tissues of humans, utilizing information from TCGA, GTEx, HPA databases, as well as samples obtained from clinical settings. Validation experiments were performed on tissue microarrays obtained from lung cancer samples. We examined targeted next-generation sequencing data from the TCGA database to gain insight into the frequency of TREML1 mutations and the collection of genes that are co-altered in tumors with TREML1 mutations. Results Our findings reveal that TREML1 is highly expressed in lung cancer, and could be one valueable predictor which may be applied in clinic in the future. Analysis of survival data from the TCGA and GTEx database suggests that high levels of TREML1 expression are associated with poor clinical prognosis in lung cancer. Analysis of gene mutations revealed that TTN (53.7%) is the most frequent alteration associated with TREML1 overexpression in LUAD, while APOB is the most common alteration in LUSC. Conclusions It can be concluded that TREML1 is a suitable target for prognosis and treatment markers. Additional research is required to comprehensively grasp how TREML1 interacts with these signaling pathways, which will be the primary focus of our upcoming studies. TREML1 mRNA-sequencing biomarker prognosis lung cancer. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Lung cancer ranks among the top causes of cancer cases and deaths globally. 1 Approximately 2.2 million new instances of lung cancer and 1.8 million fatalities were recorded worldwide in 2020. 2 This disease is intricate and varied, categorized as small-cell lung carcinoma (SCLC) or non-small-cell lung carcinoma (NSCLC) based on tissue origins and tumor behaviors. 3 Given its fast progression and grim outlook, early and precise diagnosis of lung cancer is crucial for successful treatment. Various clinical methods have been developed to aid doctors in diagnosing lung cancer, including tumor-related biomarkers, chest radiography, computed tomography (CT), and pathological examination. 4 CT scans are now the most commonly used non-invasive method for early cancer detection. Additionally, studies have shown that CT synthetized PET scans have promising diagnostic value in cancer staging, improving early detection, risk stratification, and prognostication. 5 Deep learning models based on CT have also been able to identify high-risk patients with clinical stage IA NSCLC who have undergone segmentectomy. 6 However, early-stage tumors may not exhibit typical symptoms, making it challenging to detect subtle pathological changes through visual assessments.For the most precise cancer subtype diagnoses in clinical settings, it is strongly advised to conduct pathological examination as an additional complementary test.It is important to mention that pathological examinations can be invasive, as they involve the collection of tissue samples through needle biopsy or surgical removal. Research has shown that tumor-related genetic information can be released into body fluids during tumor formation and development, potentially aiding in overcoming obstacles and playing a crucial role in early diagnosis. 7 Jie He and colleagues 8 demonstrated the diagnostic significance of cell-free immune-related miRNAs (cf-IRmiRNAs) in detecting cancer early and their potential influence on medical practice.Interestingly, from a technical perspective, cell-free DNA (cfDNA) methylation analysis is positioned as a promising clinical instrument for lung cancer screening, early detection, prediction, and therapy, based on its capacity to distinguish between cancerous and normal cells. 9 Research has shown that malignant cells release exosomes into the bloodstream, and exosomal RNAs may serve as a reliable biomarker to indicate alterations in cancer cells as the tumor advances. 10 Additionally, exosomal miRNAs have been shown to play crucial roles in cell-to-cell communication and tumor advancement. 11 This study involved analyzing RNA sequencing (RNA-seq) data to pinpoint genes linked to unfavorable prognosis in lung cancer patients. Next, we examined information from two publicly available tumor datasets (TCGA and GTEx) to verify the results of our own samples. Identifying genes linked to patient outcomes involved comparing sequencing data from self-test samples in clinical cohorts with information from a public database. Our research showed that TREML1 expression varies across different types of lung cancer and could be a predictor of a negative outcome for lung cancer patients. NGS was incorporated to analyze the genomic characteristics, transcriptomic profile, and survival outlook of TREML1 in early-stage lung cancer patients using an online platform. We provided clinical data of our independent research queue of west china hospital and the larger TCGA and GTEx cohort for diagnosis in lung cancer. A novel biomarker, TREML1 , was discovered in exosomes derived from tumors to predict lung cancer, potentially aiding in the identification of diagnostic and prognostic indicators, as well as molecular targets for treatment, particularly in the early stages of lung cancer. 2 Materials and methods 2.1 Sample acquisition A total of thirty individuals with lung cancer were included in the study, with sixteen having early-stage cancer and twelve having advanced-stage cancer.Twenty regular samples were collected from twenty individuals to correspond with neighboring healthy tissues at West China Hospital of Sichuan University.Patients' stages were categorized based on the recommendations of the American Joint Commission on Cancer (AJCC) 8th edition. Prior to collecting biospecimens, all patient parents or legal guardians provided written informed consent for future research. There will be no payment for taking part in this research project. Before isolating nucleic acids, a pathologist reviewed sections of all frozen biospecimens and confirmed that they contained more than 50% viable tumor cells. To isolate platelets, start by centrifuging 200g of fresh blood for 10 minutes. Next, transfer the upper plasma into a 15ml centrifuge tube and then move the platelets into a 1.5ml centrifuge tube. Centrifuge the platelets at 800g for 10 minutes and use the miRNeasy Mini kit (QIANGEN) to extract the RNA. For plasma isolation, centrifuge the plasma at 1600g in a 15ml centrifuge tube for 10 minutes. Extract the exosomes using the exoEasy Maxi Kit and then determine the RNA concentration with qubit. Plasma isolation: After centrifugation of 15 ml tube at 1600g for 10 min, exosomes were extracted using exoEasy Maxi Kit, and then RNA was extracted using miRNeasy Mini kit (QIANGEN), and the concentration was determined using qubit. The Frontiers Science Center isolated genomic DNA and total-RNA for the Disease-related Molecular Network.DNA extraction kits were utilized to isolate DNA, while RNA isolation was performed using Trizol. DNA quantity was measured with PicoGreen and assessed for quality by visualization in agarose gel. RNA quantity was determined with a Qubit fluorometry assay, while RNA quality and integrity were assessed through RNA integrity number (RIN) measurements conducted on an Agilent Bioanalyzer System. One ug of RNA was reverse transcribed using a Transcriptor First Strand cDNA Synthesis Kit (Roche). The RT-qPCR analysis was conducted with a CFX Connect Real-Time PCR Detection system from Bio-Rad. 2.2 mRNA-sequencing data processing To process mRNA-seq data, reads of poor quality were removed based on the criteria: ( 1 ) 'N' bases making up over 10% of the read's length, ( 2 ) reads consisting entirely of 'A' bases, ( 3 ) bases with a quality score < 5 making up more than 50% of the read's length, ( 4 ) sequences with a length less than 20 nucleotides, or ( 5 ) presence of adaptor sequences. 2.3 Differentially expressed genes analysis The gene expression matrices were filtered again to eliminate genes with low expression (zero counts in less than two-thirds of all samples). Approximately 33% of the genes in every eligible expression matrix had at least 10 counts and were ready for analysis of differential gene expression.The samples were grouped into thethe exosomes group (RNA derived from exosomes) and platelet group (RNA derived from platelets).The examination utilized the DESeq2 software (v1.12.3) within the R platform (v4.0.3). DEGs were identified based on two Padj conditions.The significance level is less than 0.05 and the absolute value of the logarithm base 2 of the fold change is greater than 2. To explore the enrichment of differentially expressed genes (DEG) in specific clusters, enrichment analysis was conducted with the R package ClusterProfiler. This analysis included gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Disease Ontology (DO), and Reactome. Visualization was performed using the pheatmap version 1.0.1 and ggplot2 version 3.3.2 packages. 2.4 TREML1 expression profile data analysis Raw RNAseq data counts were obtained from the TCGA and GTEx portal, in addition to relevant clinical information.After processing the original data, counts were transformed into transcripts per million (TPM) and normalized with log2 (TPM + 1).Tissue samples with corresponding clinical data were kept, creating a dataset of these samples for future analysis. TREML1 expression was compared between tumour and their paired adjacent normal tissues across various tumour types present in the TCGA database. The statistical differentiation was assessed by Wilcoxon signed-rank test. The TCGA cancer types lacking corresponding normal sample were excluded from this analysis. To supplement the data from TCGA database, we also utilised a proteomic database—Clinical Proteomic Tumor Analysis Consortium (CPTAC) 12 to access TREM’s protein expression.Validating proteins offers an advantage because, unlike many databases that focus on mRNA levels, CPTAC provides a more precise representation of the disease's physiological condition. The 'Expression analysis-stage Plot' module of Gene Expression Profiling Interactive Analysis (GEPIA2, version 2) online tool was used to display violin plots showing TREM expression across various pathological stages (http//gep ia2.cancer-pku.cn/#analysis). 13 2.5 The human protein atlas Information regarding the expression of TREML1 in cancer cell lines and cancer tissues was acquired from the human protein atlas (HPA) website. 14 2.6 Survival analysis Survival analysis was conducted on TREML1 in all TCGA cohorts using the 'Survival Analysis' tool in GEPIA2 to analyze overall survival (OS) and disease-free survival (DFS), resulting in the generation of survival maps and plots. 15 Kaplan-Meier curves were created using the 'survminer' package.In order to assess survival outcomes, we investigated the influence of TREML1 on survival in different types of cancer, utilizing the Mantel-Cox test for analysis and comparison.Survival plots separated cohorts into high and low TREML1 expression levels using median values. 2.7 Genetic mutation analysis In the cBioPortal for Cancer Genomics (http//cbioportal.org), we input 'TREM' into the 'Quick Search' area. 16,17 The 'Cancer Types Summary' module displayed the alteration frequency for all tumor types in the TCGA database. Mutations in TREM were shown in the protein structure schematic and 3D structure, both available in the 'Mutations' section.Kaplan-Meier graphs were displayed for certain TCGA cancer categories, with and without changes in TREML1 genes, utilizing the 'Comparison/Survival' tool. 2.8 Experimental validation Validation experiments were conducted on tissue microarrays obtained from lung tumors to analyze immunohistochemistry (IHC) staining. 2.9 Statistical analysis Statistical analyses were performed using GraphPad Prism 9.0 software (GraphPad Software). The Student’s t-test was employed for two-group comparisons, while one-way ANOVA non-parametric was used for comparisons involving three or more groups. Statistical signifcance was indicated by p values < 0.05. Detailed p-values can be found in the fgures. 3 Results 3.1 RNA derived from exosome and platelet have different gene expression profiles Comparison of transcriptomes in early lung cancer and normal groups identified 541 genes with differential expression (DEG; 208 increased and 333 decreased; p value < 0.05) in exosome group, and 10292 genes with differential expression (DEG; 4581 increased and 5711 decreased; p value < 0.05) in platelet group.Comparison of transcriptomes in the early lung cancer group and the normal group revealed 1177 genes with differential expression (DEG; 1023 increased and 154 decreased; p value < 0.05) in the exosome group, and 14393 genes with differential expression (DEG; 3703 increased and 10686 decreased; p value < 0.05) in the platelet group (Fig. 1 A-D). Gene functional enrichment analysis was performed on potential targets using GO, KEGG, DO Enrichment Analysis, and Reactome Enrichment Analysis to explore their function. Supplement Figure-1 shows that which pathways the up-regulated genes contributed to through the exosone group (early lung cancer vs . normal patient, and advanced lung cancer vs . the normal patient) through GO Enrichment Analysis. KEGG, DO, Reactome Enrichment Analysis showed the correlated pathways consistent with GO Enrichment Analysis. Futhermore, Venn intersection analysis on four sets of differentially expressed genes by regular method showed 44 common differential gene expression (Fig. 1 E), and 37 upregulated candidate genes were selected to be conducted further analysis. 37 candidate genes, namely, AC093909.1 , BSG , C9orf16 , CALM3 , CLDN5 , CLU , CMTM5 , CTSA , DDX11L10 , DDX11L5 , DOK2 , FCER1G , GPX1 , H2BC12 , H4C9 , HCFC1R1 , HLA-C , HLA-H , IFI27L2 , ITM2B , MCEMP1 , MEA1 , NDUFA6 , NDUFAF3 , PF4 , POLR2E , PTCRA , RHOC , RUFY1 , SAT1 , SH3BGRL3 , SPARC , SPX , TMEM106C , TREML1 , YIF1B , YWHAZP2 were identified ( Supplement Fig. 2 ). Venn intersection analysis by log2FoldChange showed no common differential gene expression (Fig. 1 F). We obtained expression data from the TCGA and GTEx databases to analyze the mRNA levels of 37 potential genes in cancer and normal tissues. The findings revealed a marked increase in TREML1 expression in cancerous tissues compared to normal tissues in both the exosome and platelet groups (Fig. 1 G, 1 H, Supplement Fig. 3 – 4 ). Consistently, the clinical and molecular characteristics of TREML1 in lung cancer was validated in the later exploration. 3.2 Expression and prognosis profile of TREML1 Figure 2 A shows a notable increase in TREML1 expression in lung cancer, specifically in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), compared to their corresponding normal samples. Figure 2 B illustrates a significant difference in TREML1 expression among various pathological stages, suggesting a strong correlation between TREML1 and the clinical stage of lung cancer (p < 0.05). Our findings suggested that increased TREML1 levels were associated with a poor outlook in terms of overall survival (OS) (Fig. 2 C) and disease-free survival (DFS) (Fig. 2 D) in lung cancer patients from the TCGA dataset.Immunohistochemistry analysis revealed that TREML1 exhibited decreased levels in lung cancer tissue samples (Fig. 2 E). 3.3 TREML1 mutation analysis Figure 3 A, obtained from cBioPortal, shows the genetic changes in TREML1 for samples from patients. The highest alteration frequency of TREML1 occurs among patients with Esophageal adenocarcinoma (EAC), where the primary type of alteration was ‘amplification’ referring to copy number alteration (CNA). Six types of cancer with genetic mutations showed significant amplification of TREML1 : stomach adenocarcinoma (STAD), ovarian serous cystadenocarcinoma (OVs), diffuse large B-Cell lymphoma (DLBCL), skin cutaneous melanoma (SKCM), cholangiocarcinoma, and lung adenocarcinoma. In Fig. 3 B,C, the TCGA cohort displayed multiple mutation sites in TREML1 , highlighting the site with the highest frequency of alteration (N290K) in the V-set within the 3D structure of TREML1 . A single sample from the TCGA repository exhibited a mutation at this common location in a case of LUAC. Nevertheless, the N290K mutation in TREML1 had no impact on the overall survival of patients with LUAC (Fig. 3 D). 3.4 The correlation between clinical characteristic and TREML1 expression To improve comprehension of the relationship between TREML1 expression and clinical characteristics in various types of cancer. Sangerbox 3.0 online tools are utilized to analyze the relationship between important clinical features and TREML1 expression in TCGA dataset. Notable variances in TREML1 expression levels were noted among different groups based on proliferation, invasion, lymph node metastasis, distant metastases, total clinical stage, and gender in patients with LUAD, BRCA, ESCA, STES, KIPAN, STAD, PRAD, HNSC, LUSC, and LIHC. As shown in the Fig. 4 A- 4 D, TREML1 expression was associated with through T status, N status, M status, and total clinical stage in LUAD. Morerover, there is lower correlation with LUSC, comparing with the LUAD. Additionally, TREML1 expression significantly higer in the male patients than the female patients (Fig. 4 F). Based on the aforementioned enrichment findings, it is hypothesized that TREML1 plays a role in controlling multiple biological functions including the progression of lung cancer, the regulation of EMT, and metabolic processes. These relevent processes all have been reported to be connected to stem cells. We will delve deeper into the cellular stemness of TREML1 in lung cancer to determine if TREML1 plays a significant regulatory role in stem cells. Figure 4 E shows the standardized pan-cancer dataset obtained from 37 tumors. In particular, we collected the TREML1 (ENSG00000161911) expression data from every sample and calculated the RNA stemness scores for each tumor using mRNA characteristics. Next, we computed the Pearson correlation coefficients for each type of tumor and found significant positive correlations in 18 tumors, such as LUAD and LUSC, as well as negative correlations in 15 tumors.Thus, our initial results indicate that TREML1 could have a positive impact on the regulation of stem cells in lung cancer, however, additional experimental confirmation is required. 3.5 Genetic characteristics of TREML1 mutation in NSCLC A large cohort of 995 patients with lung cancer and NGS genomic profiling were enrolled.Among these included patients, 508 were lung adenocarcinoma, of which 255 with low expression of TREML1 and 253 with high expression; 487 were lung squamous cell carcinoma, of which 244 with low expression of TREML1 and 243 with high expression.Lollipop map of hotspot mutations in TREML1 protein present that approximately 0.8% TREML1 mutation ocuurred in lung adenocarcinoma, 0.6% TREML1 mutation ocuurred in lung squamous cell carcinoma (Fig. 5 A). The genomic profile of TREML1 mutation from the TCGA cohort was different between LUAD and LUSC.The most co-mutation of TREML1 was TTN (53.7%), and then RYR2 (42.8%), LRP1B (37.8%), USH2A (36.8%), ZFHX4 (35.9%), ZNF536 (23.5%), CSMD1 (22.8%), COL11A1 (22.8%)(Fig. 5 B) for lung adenocarcinoma. APOB ranked at the top (20.7%) followed by CSMD (20.4%), CTNNA (16.1%), KCNH7 (12.9%), DCDC1 (12.4%), CTNND2 (12.1%), ADGRL3 (11.8%), UNC79 (10.9%) (Fig. 5 C). 4 Discussion The involvement of the TREM-like transcript-1 (TLT-1) factor family in lung cancer is seldom discussed.In this study, we demonstrated that TREML1 expression level was higher in tumors than in normal lungs by combining mRNA sequencings of self-test samples and the public experiments on patients’ specimens. TLT-1 contains an IgV domain, 18 similar to other soluble immunocheckpoint ligands/receptors, 19 and is one of the most abundant transcripts found in platelets. 20 Our preliminary studies have shown that we can detect the mRNA from platelets while simultaneously detecting exosomes. We discovered upregulated genes that show differential expression in exosomes and platelets, identified a group of 37 genes present in the list of differentially expressed genes for early lung cancer compared to healthy individuals, and for advanced lung cancer compared to healthy individuals in both exosomes and platelets. In line with a prior study 21 that showed in mice with compatible tumors, the interaction between TLT-1 and T cells resulted in the inhibition of CD8 T cells, thus supporting tumor development. It is worth mentioning that the majority of these pathways are connected to the field of tumor immunology in our study. To do so, we analyzed transcriptomic data from two large independent publicly database (TCGA and GTEx) for external verification. Our findings indicated that TREML1 levels may increase in reaction to tumor immunology during the transition from early to late stages of lung cancer. Nevertheless, the role of TREML1 in the advancement of lung cancer remains uncertain. Pathological and functional analyses were conducted to demonstrate the crucial involvement of TREML1 in the advancement of lung cancer. The authenticity of the signature gene TREML1 was confirmed through immunohistochemistry, staining outcomes, and examination of its protein structure in HPA databases. Initial findings indicated that platelet activity in sepsis identified the receptor, triggering receptor expressed on myeloid cells (TREM)-like transcript-(TLT)-1, as a crucial factor in the advancement of sepsis. 22 Chia-Ming Chang et al 23 revealed a new route in which platelets oppose immune responses to infection via TLT-1, and they have conducted research on how sTLT-1 interacts with and influences the host immune system during sepsis. Additionally, research has shown that TREML1 and TREML2 could increase the risk of Alzheimer's disease by influencing both amyloid-β pathology and neuronal degeneration, potentially serving as neurodegenerative markers. 24 Understanding the genomic characteristics, transcriptomic profile, and survival outlook of TREML1 is crucial for predicting tumorigenesis in lung cancer and developing effective therapeutic strategies. In this study, we thoroughly examined the frequency and genetic variability of TREML1 in human cancer using a resolution for mutant alleles, while also taking into account host factors such as age, TNM stage, tumor stemness, and gender. High expression of TREML1 was found to be a risk factor in the development and advancement of various cancers, including esophageal adenocarcinoma, stomach adenocarcinoma, ovarian serous cystadenocarcinoma, diffuse large B-Cell lymphoma, skin cutaneous melanoma, cholangiocarcinoma, sarcoma, bladder urothelial carcinoma, and others. The highest alteration frequency of TREML1 is N290K in the V-set domain. We further investigate the prognostic predictive ability of TREML1 N290K by analyzing OS, PFS, DFS, and DSS. The results demonstrated that the TREML1 N290K woud not accelerate the occurrence of tumors. Obviously, we linked the distinct molecular tracks of TREML1 N290K lung cancer would not lead to differential clinical outcomes. We have plotted a waterfall plot of co-expression of this TREML1 both in LUAD and LUSC. Analysis of gene mutations revealed that TTN (53.7%) is the most frequent alteration associated with TREML1 overexpression in LUAD, while APOB is the most common alteration in LUSC. The study still has some limitations.Because there are not enough clinical cases and limited samples, we were unable to sequence each group coherently, so further exploration may be necessary in a larger sample size to uncover differential mutation or expression patterns. Likewise, this research is limited to a single institution, potentially introducing bias that could be mitigated by including a larger sample size in future studies. Secondly, there were several in-depth mechanism has not been explored and explained clearly. We could not explain what transcription foctor the TREML1 promotes ultimately leading to enhanced stemness. Due to limitations in sample size, we could not unable the meaningful mutations with RNA-sequencing of self-test samples and public database, futhermore, the specific tumor-promoting variants could not be present and then we may not develop specific targeted drugs to the TREML1 in the future. Our current research provided a more thorough understanding of TREML1 's role in lung cancer development. The results of our study indicate that TREML1 is a suitable target for prognostic and therapeutic markers. Additional research is required to comprehensively grasp how TREML1 interacts with these signaling pathways, which will be the primary focus of our upcoming studies. 5 Conclusions In summary, after examining clinical cohort and cellular RNA-seq information, it was determined that elevated TREML1 levels are linked to the development of tumors in lung cancer. Our findings unequivocally identify TREML1 as a potential predictor implicated in the advancement of lung cancer, warranting additional research into the mechanisms through which TREML1 facilitates the progression of early-stage lung cancer. Declarations Author contributions Dan Liu, Jiadi Gan conceived the project and designed the experiments. Wenliang Qiao, Juan Chen, Yongfeng Yang, Wang Hou, Kaixin Lei collected the samples, performed sequencing experiments, and processed the data. Haibo Wang, Guonian Zhu, Jinghong Xian, Zhoufeng Wang performed the experiments. Wenliang Qiao, Jiadi Gan, Dan Liu conceived the study and wrote the manuscript. All authors have read and approved the final manuscript. Funding Funding for this research was provided by the National Natural Science Foundation of China (grant number 92159302), the Science and Technology Project of Sichuan (grant number 2022ZDZX0018), the National Natural Science Foundation of China (grant number 82173182), the Science and Technology Program of Sichuan (grant number 2023NSFSC1939), and the 1.3.5 Project for Disciplines of Excellence at West China Hospital, Sichuan University (grant number ZYJC 21054). The primary research and development initiatives of the Sichuan Provincial Department of Science and Technology assigned to W.L.Q in project 2020YFS0251. Data availability The data that support the findings of this study are available onrequest from the corresponding author. The information is unavailable to the public because of privacy or ethical limitations. Conflict of interest The writers assert that they have no conflicts of interest. Ethics approval and consent to participate The West China Hospital Research Ethics Committee and ethics committees approved this study, which did not interfere with clinical management. Informed consent (oral or written) was obtained from study participants according to local requirements, except for cases in which a local committee granted a waiver or exemption. We adhered to the Declaration of Helsinki and Good Clinical Practice guidelines. References Siegel RL, Giaquinto AN, Jemal A, Cancer statistics (2024) CA Cancer J Clin 2024 Jan-Feb 74(1):12–49 Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249 Minna JD, Roth JA, Gazdar AF (2002) Focus on lung cancer. Cancer Cell 1(1):49–52 Prabhakar B, Shende P, Augustine S (2018) Current trends and emerging diagnostic techniques for lung cancer. 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J Exp Med 220(1):e20212218 Washington AV, Gibot S, Acevedo I et al (2009) TREM-like transcript-1 protects against inflammation-associated hemorrhage by facilitating platelet aggregation in mice and humans. J Clin Invest 119(6):1489–1501 Chang CM, Cheng KH, Wei TY et al (2023) Soluble TREM-like Transcript-1 Acts as a Damage-Associated Molecular Pattern through the TLR4/MD2 Pathway Contributing to Immune Dysregulation during Sepsis. J Immunol 210(9):1351–1362 Tian ML, Ni XN, Li JQ et al (2019) Alzheimer’s Disease Neuroimaging Initiative. A candidate regulatory variant at the TREM gene cluster confer Alzheimer's Disease risk by modulating both amyloid-β pathology and neuronal degeneration. Front Neurosci 13:742 Additional Declarations No competing interests reported. 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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-4616157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324374102,"identity":"710c1e58-f2d0-47b6-b8cc-bbe1d9cc9250","order_by":0,"name":"Wenliang Qiao","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wenliang","middleName":"","lastName":"Qiao","suffix":""},{"id":324374103,"identity":"b61d5f89-41c7-429c-b03c-b23195040c9f","order_by":1,"name":"Juan Chen","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Chen","suffix":""},{"id":324374104,"identity":"bdf23fbd-e235-4f7c-b217-79b34df0dbd2","order_by":2,"name":"Yongfeng Yang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Yang","suffix":""},{"id":324374105,"identity":"1294b095-32af-4fb8-9eea-761a4f32922d","order_by":3,"name":"Wang Hou","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Hou","suffix":""},{"id":324374106,"identity":"ddaa1a8b-49f8-465d-a385-405776018146","order_by":4,"name":"Kaixin Lei","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Kaixin","middleName":"","lastName":"Lei","suffix":""},{"id":324374107,"identity":"d49db99f-48d7-4f45-9391-c68e4ade8ef7","order_by":5,"name":"Haibo Wang","email":"","orcid":"","institution":"Hangzhou Third People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Wang","suffix":""},{"id":324374108,"identity":"279a0f25-a219-43b3-bc59-b0cec474ef41","order_by":6,"name":"Guonian Zhu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Guonian","middleName":"","lastName":"Zhu","suffix":""},{"id":324374111,"identity":"0847bd65-2023-48be-98b0-fe1a5ffa2be9","order_by":7,"name":"Jinghong Xian","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jinghong","middleName":"","lastName":"Xian","suffix":""},{"id":324374115,"identity":"a9724cd1-87a3-4b43-9b88-a75555035e0b","order_by":8,"name":"Zhoufeng Wang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhoufeng","middleName":"","lastName":"Wang","suffix":""},{"id":324374117,"identity":"1c463c38-f83e-463e-af4f-64527351886f","order_by":9,"name":"Jiadi Gan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACgwMMBiCameF4Y+PDD8RosYRrOXO42ViCGC32UC0MDDfS2wR4iNFidrx5A+OXmjvsfDcftjFIMNjJ6TYQ0nLmWAGzzLFnzJK3E9seFDAkG5sdIKTlRo4BswTbYWaD24ntBhIMBxK3EdJicP8NUMs/oJabB9skeIjScoPHgPFjG1DLDUZitZxJK2Bm7DvMLHkmERjIBkT4xeD44Q2MP74dTuY7fvzhww8VdnIEtQAB+29gdCRDTSCsHAwYfzAw2BGpdhSMglEwCkYiAACpP0m0sWev+QAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Jiadi","middleName":"","lastName":"Gan","suffix":""},{"id":324374121,"identity":"f3980548-d8d4-41b0-898d-5656fe34256e","order_by":10,"name":"Dan Liu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-21 08:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4616157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4616157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60155591,"identity":"449c55ab-496c-4626-8ec5-44c1743d503d","added_by":"auto","created_at":"2024-07-12 11:52:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":731772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of Tumor-originated exosomal and platelets \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTREML1\u003c/strong\u003e\u003c/em\u003e. (A-D), Volcano plot showing upregulated and downregulated genes in the exosome group and the platelet group. The early lung cancer patient group versus the normal patient group. The advanced lung cancer group versus the normal patient group. The abscissa indicates gene ratio and the enriched pathways were presented in the ordinate. Colors represent the signifcance of diferential enrichment, the size of the circles represents the number of genes analyzed, the larger the circle, the greater the number of genes. In the enrichment result, P\u0026lt;0.05 or FDR\u0026lt;0.05 is considered to be a meaningful pathway. (E-H) GO analysis of potential targets of mRNAs, the biological process, cellular component, and molecular function of potential targets were clustered based on ClusterProfler package in R software (version: 3.18.0). (I-L) The enriched KEGG signaling pathways were selected to demonstrate the primary biological actions of major potential mRNA. (M-P) Pathways significantly enriched in exosome group and the platelet group according to DO database. (Q-T) Several significantly upregulated pathways in the exosome group and the platelet group identified by functional annotations of DEGs to the Reactome database. (U) Venn diagram of upregulated DEGs of exosome group and the platelet group by regular analysis method. (V) Venn diagram of upregulated DEGs of exosome group and the platelet group by log2FoldChange. (W) Box plots illustrating expression analysis of \u003cem\u003eTREML1 \u003c/em\u003ein three major subclasses in exosome group. (X) Box plots illustrating expression analysis of \u003cem\u003eTREML1\u003c/em\u003e in three major subclasses in platelet group. Abbreviation: DEG differentially expressed genes; DO Disease Ontology; GO gene ontology; KEGG Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/899c7797dce1c8c60494b9a5.jpg"},{"id":60154837,"identity":"39f9a026-cb70-4c02-88f8-7531034ffe50","added_by":"auto","created_at":"2024-07-12 11:44:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":360755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between triggering receptor expressed on myeloid cells (TREM)-like transcript (TLT)-1 expression and survival outcome of lung cancer in The Cancer Genome Atlas (TCGA) database\u003c/strong\u003e. (A) Compared with paired normal samples, \u003cem\u003eTREML1\u003c/em\u003e expression levels in tumour samples from LUAD and LUSC were obtained based on TCGA data. (B) The \u003cem\u003eTREML1\u003c/em\u003e expression levels were detected according to pathological stage in lung cancer (LC) (P \u0026lt; 0.05). Only statistically significant differences were shown. log2 (TPM + 1) was calculated for log-scale, where TPM represents transcripts per million. According to log10 (hazard ratio [HR]), overall survival (C) and disease-free survival (D) for different patient cohorts were displayed on the survival maps. Kaplan-Meier curves with positive results were shown (P \u0026lt; 0.05). (E) Representative images of \u003cem\u003eTREML1\u003c/em\u003eexpression in cancer tissue samples were shown. There were two representative immunohistochemistry images for lung cancer. *P \u0026lt; 0.05. Abbreviation: LUAD Lung adenocarcinoma; LUSC lung squamous carcinoma, TCGA The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/2a12f5791d9bbd25525c6931.jpg"},{"id":60156046,"identity":"c97473a4-839b-43b8-8e63-c427143ecc94","added_by":"auto","created_at":"2024-07-12 12:00:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":436123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic alteration of triggering receptor expressed on myeloid cells (TREM)-like transcript (TLT)-1 in different tumours based on TCGA database by using the cBioPortal tool\u003c/strong\u003e. The alteration frequency with mutation type (A) and mutation site (B) for multiple cancers were shown. The mutation site with the highest alteration frequency (N290K) in the V-set domain of \u003cem\u003eTREML1\u003c/em\u003e (C). The correlation between \u003cem\u003eTREML1\u003c/em\u003e mutation status and overall, disease-specific, disease-free and progression-free survival of lung cancer were analysed (D). Abbreviation: TCGA the Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/76acda1c15d16a5035b62bb1.jpg"},{"id":60154834,"identity":"a166370a-20b2-4ab6-a829-e9ac653a4175","added_by":"auto","created_at":"2024-07-12 11:44:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":416639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between clinical characteristic and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTREML1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTREML1\u003c/em\u003e expression levels in different tumor types from TCGA database were analyzed by Sangerbox 3.0; Representative boxplots display the correlation analysis of the \u003cem\u003eTREML1\u003c/em\u003eexpression levels and clinical T-staging (A), N-staging (B), M-staging (C), I-IV-staging (D). The lollipop plot depicts the correlation between \u003cem\u003eTREML1\u003c/em\u003eand tumor stemness in pan-cancer. The x-axis represents the correlation between \u003cem\u003eTREML1\u003c/em\u003e expression and RNA stemness, while the y-axis illustrates different types of tumors. Positive values indicate a positive correlation, whereas negative values indicate a negative correlation (E). The correlation of \u003cem\u003eTREML1\u003c/em\u003e expression and gender (F). * P\u0026lt;0.05; ** P\u0026lt;0.01; *** P\u0026lt;0.001.Abbreviation: TCGA the Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/72f9324ec7c84d4dbdc8e512.jpg"},{"id":60155594,"identity":"5c5913b3-626b-4bed-92a7-d53e67d31519","added_by":"auto","created_at":"2024-07-12 11:52:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":848687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic characteristics of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTREML1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emutation in NSCLC\u003c/strong\u003e. (A) Lollipop map of hotspot mutations in \u003cem\u003eTREML1\u003c/em\u003eprotein across several tumor. Genomic landscape of \u003cem\u003eTREML1\u003c/em\u003e mutation in LUAD (B) and LUSC (C). Abbreviation: NSCLC non-small cell lung cancer.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/5884ef084e230b90a0ba9e22.jpg"},{"id":75749526,"identity":"af5a406c-c053-48b8-a7b8-028bda21b1fb","added_by":"auto","created_at":"2025-02-07 19:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3821202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/a3733075-d950-4479-9f50-7521824100f3.pdf"},{"id":60156047,"identity":"a8c4c270-8692-4ae2-8175-d18dfb749389","added_by":"auto","created_at":"2024-07-12 12:00:21","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1107943,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/ae92065682fe58fa0ab62fed.pdf"},{"id":60154839,"identity":"2d399362-3857-4296-b5f9-bd33ecebdbfd","added_by":"auto","created_at":"2024-07-12 11:44:21","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":266339,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/c2f14c4faa64b7ef79d624ec.pdf"},{"id":60155596,"identity":"7fee06d0-527a-4ea5-91fd-df8459b97c22","added_by":"auto","created_at":"2024-07-12 11:52:21","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":266491,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/be06e79cbad8cfc75f166257.pdf"},{"id":60154844,"identity":"16635d37-d710-403e-bbd9-d0f2f471f329","added_by":"auto","created_at":"2024-07-12 11:44:22","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":6358822,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/529cc42879fcfd4b300ee460.pdf"},{"id":60154842,"identity":"ec10ccb5-36a8-4a5d-9187-9202f57e9d91","added_by":"auto","created_at":"2024-07-12 11:44:21","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":6151941,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4616157/v1/8a3cc5d70177fe85cbd86d42.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumor-originated exosomal TREML1 is a novel predictive biomarker for tumorigenesis in lung cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer ranks among the top causes of cancer cases and deaths globally.\u003csup\u003e1\u003c/sup\u003e Approximately 2.2\u0026nbsp;million new instances of lung cancer and 1.8\u0026nbsp;million fatalities were recorded worldwide in 2020.\u003csup\u003e2\u003c/sup\u003e This disease is intricate and varied, categorized as small-cell lung carcinoma (SCLC) or non-small-cell lung carcinoma (NSCLC) based on tissue origins and tumor behaviors.\u003csup\u003e3\u003c/sup\u003e Given its fast progression and grim outlook, early and precise diagnosis of lung cancer is crucial for successful treatment.\u003c/p\u003e \u003cp\u003eVarious clinical methods have been developed to aid doctors in diagnosing lung cancer, including tumor-related biomarkers, chest radiography, computed tomography (CT), and pathological examination.\u003csup\u003e4\u003c/sup\u003e CT scans are now the most commonly used non-invasive method for early cancer detection. Additionally, studies have shown that CT synthetized PET scans have promising diagnostic value in cancer staging, improving early detection, risk stratification, and prognostication.\u003csup\u003e5\u003c/sup\u003e Deep learning models based on CT have also been able to identify high-risk patients with clinical stage IA NSCLC who have undergone segmentectomy.\u003csup\u003e6\u003c/sup\u003e However, early-stage tumors may not exhibit typical symptoms, making it challenging to detect subtle pathological changes through visual assessments.For the most precise cancer subtype diagnoses in clinical settings, it is strongly advised to conduct pathological examination as an additional complementary test.It is important to mention that pathological examinations can be invasive, as they involve the collection of tissue samples through needle biopsy or surgical removal.\u003c/p\u003e \u003cp\u003eResearch has shown that tumor-related genetic information can be released into body fluids during tumor formation and development, potentially aiding in overcoming obstacles and playing a crucial role in early diagnosis.\u003csup\u003e7\u003c/sup\u003e Jie He and colleagues\u003csup\u003e8\u003c/sup\u003e demonstrated the diagnostic significance of cell-free immune-related miRNAs (cf-IRmiRNAs) in detecting cancer early and their potential influence on medical practice.Interestingly, from a technical perspective, cell-free DNA (cfDNA) methylation analysis is positioned as a promising clinical instrument for lung cancer screening, early detection, prediction, and therapy, based on its capacity to distinguish between cancerous and normal cells.\u003csup\u003e9\u003c/sup\u003e Research has shown that malignant cells release exosomes into the bloodstream, and exosomal RNAs may serve as a reliable biomarker to indicate alterations in cancer cells as the tumor advances.\u003csup\u003e10\u003c/sup\u003e Additionally, exosomal miRNAs have been shown to play crucial roles in cell-to-cell communication and tumor advancement.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study involved analyzing RNA sequencing (RNA-seq) data to pinpoint genes linked to unfavorable prognosis in lung cancer patients. Next, we examined information from two publicly available tumor datasets (TCGA and GTEx) to verify the results of our own samples. Identifying genes linked to patient outcomes involved comparing sequencing data from self-test samples in clinical cohorts with information from a public database. Our research showed that \u003cem\u003eTREML1\u003c/em\u003e expression varies across different types of lung cancer and could be a predictor of a negative outcome for lung cancer patients. NGS was incorporated to analyze the genomic characteristics, transcriptomic profile, and survival outlook of \u003cem\u003eTREML1\u003c/em\u003e in early-stage lung cancer patients using an online platform.\u003c/p\u003e \u003cp\u003eWe provided clinical data of our independent research queue of west china hospital and the larger TCGA and GTEx cohort for diagnosis in lung cancer. A novel biomarker, \u003cem\u003eTREML1\u003c/em\u003e, was discovered in exosomes derived from tumors to predict lung cancer, potentially aiding in the identification of diagnostic and prognostic indicators, as well as molecular targets for treatment, particularly in the early stages of lung cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample acquisition\u003c/h2\u003e \u003cp\u003eA total of thirty individuals with lung cancer were included in the study, with sixteen having early-stage cancer and twelve having advanced-stage cancer.Twenty regular samples were collected from twenty individuals to correspond with neighboring healthy tissues at West China Hospital of Sichuan University.Patients' stages were categorized based on the recommendations of the American Joint Commission on Cancer (AJCC) 8th edition. Prior to collecting biospecimens, all patient parents or legal guardians provided written informed consent for future research. There will be no payment for taking part in this research project. Before isolating nucleic acids, a pathologist reviewed sections of all frozen biospecimens and confirmed that they contained more than 50% viable tumor cells. To isolate platelets, start by centrifuging 200g of fresh blood for 10 minutes. Next, transfer the upper plasma into a 15ml centrifuge tube and then move the platelets into a 1.5ml centrifuge tube. Centrifuge the platelets at 800g for 10 minutes and use the miRNeasy Mini kit (QIANGEN) to extract the RNA. For plasma isolation, centrifuge the plasma at 1600g in a 15ml centrifuge tube for 10 minutes. Extract the exosomes using the exoEasy Maxi Kit and then determine the RNA concentration with qubit. Plasma isolation: After centrifugation of 15 ml tube at 1600g for 10 min, exosomes were extracted using exoEasy Maxi Kit, and then RNA was extracted using miRNeasy Mini kit (QIANGEN), and the concentration was determined using qubit.\u003c/p\u003e \u003cp\u003eThe Frontiers Science Center isolated genomic DNA and total-RNA for the Disease-related Molecular Network.DNA extraction kits were utilized to isolate DNA, while RNA isolation was performed using Trizol. DNA quantity was measured with PicoGreen and assessed for quality by visualization in agarose gel. RNA quantity was determined with a Qubit fluorometry assay, while RNA quality and integrity were assessed through RNA integrity number (RIN) measurements conducted on an Agilent Bioanalyzer System. One ug of RNA was reverse transcribed using a Transcriptor First Strand cDNA Synthesis Kit (Roche). The RT-qPCR analysis was conducted with a CFX Connect Real-Time PCR Detection system from Bio-Rad.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 mRNA-sequencing data processing\u003c/h2\u003e \u003cp\u003eTo process mRNA-seq data, reads of poor quality were removed based on the criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 'N' bases making up over 10% of the read's length, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) reads consisting entirely of 'A' bases, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) bases with a quality score\u0026thinsp;\u0026lt;\u0026thinsp;5 making up more than 50% of the read's length, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) sequences with a length less than 20 nucleotides, or (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) presence of adaptor sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differentially expressed genes analysis\u003c/h2\u003e \u003cp\u003eThe gene expression matrices were filtered again to eliminate genes with low expression (zero counts in less than two-thirds of all samples). Approximately 33% of the genes in every eligible expression matrix had at least 10 counts and were ready for analysis of differential gene expression.The samples were grouped into thethe exosomes group (RNA derived from exosomes) and platelet group (RNA derived from platelets).The examination utilized the DESeq2 software (v1.12.3) within the R platform (v4.0.3). DEGs were identified based on two Padj conditions.The significance level is less than 0.05 and the absolute value of the logarithm base 2 of the fold change is greater than 2. To explore the enrichment of differentially expressed genes (DEG) in specific clusters, enrichment analysis was conducted with the R package ClusterProfiler. This analysis included gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Disease Ontology (DO), and Reactome. Visualization was performed using the pheatmap version 1.0.1 and ggplot2 version 3.3.2 packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 TREML1 expression profile data analysis\u003c/h2\u003e \u003cp\u003e Raw RNAseq data counts were obtained from the TCGA and GTEx portal, in addition to relevant clinical information.After processing the original data, counts were transformed into transcripts per million (TPM) and normalized with log2 (TPM\u0026thinsp;+\u0026thinsp;1).Tissue samples with corresponding clinical data were kept, creating a dataset of these samples for future analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTREML1\u003c/em\u003e expression was compared between tumour and their paired adjacent normal tissues across various tumour types present in the TCGA database. The statistical differentiation was assessed by Wilcoxon signed-rank test. The TCGA cancer types lacking corresponding normal sample were excluded from this analysis.\u003c/p\u003e \u003cp\u003eTo supplement the data from TCGA database, we also utilised a proteomic database\u0026mdash;Clinical Proteomic Tumor Analysis Consortium (CPTAC)\u003csup\u003e12\u003c/sup\u003e to access TREM\u0026rsquo;s protein expression.Validating proteins offers an advantage because, unlike many databases that focus on mRNA levels, CPTAC provides a more precise representation of the disease's physiological condition. The 'Expression analysis-stage Plot' module of Gene Expression Profiling Interactive Analysis (GEPIA2, version 2) online tool was used to display violin plots showing TREM expression across various pathological stages (http//gep ia2.cancer-pku.cn/#analysis).\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 The human protein atlas\u003c/h2\u003e \u003cp\u003eInformation regarding the expression of \u003cem\u003eTREML1\u003c/em\u003e in cancer cell lines and cancer tissues was acquired from the human protein atlas (HPA) website.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Survival analysis\u003c/h2\u003e \u003cp\u003eSurvival analysis was conducted on \u003cem\u003eTREML1\u003c/em\u003e in all TCGA cohorts using the 'Survival Analysis' tool in GEPIA2 to analyze overall survival (OS) and disease-free survival (DFS), resulting in the generation of survival maps and plots.\u003csup\u003e15\u003c/sup\u003e Kaplan-Meier curves were created using the 'survminer' package.In order to assess survival outcomes, we investigated the influence of \u003cem\u003eTREML1\u003c/em\u003e on survival in different types of cancer, utilizing the Mantel-Cox test for analysis and comparison.Survival plots separated cohorts into high and low \u003cem\u003eTREML1\u003c/em\u003e expression levels using median values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Genetic mutation analysis\u003c/h2\u003e \u003cp\u003eIn the cBioPortal for Cancer Genomics (http//cbioportal.org), we input 'TREM' into the 'Quick Search' area.\u003csup\u003e16,17\u003c/sup\u003e The 'Cancer Types Summary' module displayed the alteration frequency for all tumor types in the TCGA database. Mutations in TREM were shown in the protein structure schematic and 3D structure, both available in the 'Mutations' section.Kaplan-Meier graphs were displayed for certain TCGA cancer categories, with and without changes in \u003cem\u003eTREML1\u003c/em\u003e genes, utilizing the 'Comparison/Survival' tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Experimental validation\u003c/h2\u003e \u003cp\u003eValidation experiments were conducted on tissue microarrays obtained from lung tumors to analyze immunohistochemistry (IHC) staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using GraphPad Prism 9.0 software (GraphPad Software). The Student\u0026rsquo;s t-test was employed for two-group comparisons, while one-way ANOVA non-parametric was used for comparisons involving three or more groups. Statistical signifcance was indicated by p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Detailed p-values can be found in the fgures.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 RNA derived from exosome and platelet have different gene expression profiles\u003c/h2\u003e \u003cp\u003eComparison of transcriptomes in early lung cancer and normal groups identified 541 genes with differential expression (DEG; 208 increased and 333 decreased; p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in exosome group, and 10292 genes with differential expression (DEG; 4581 increased and 5711 decreased; p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in platelet group.Comparison of transcriptomes in the early lung cancer group and the normal group revealed 1177 genes with differential expression (DEG; 1023 increased and 154 decreased; p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the exosome group, and 14393 genes with differential expression (DEG; 3703 increased and 10686 decreased; p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the platelet group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene functional enrichment analysis was performed on potential targets using GO, KEGG, DO Enrichment Analysis, and Reactome Enrichment Analysis to explore their function. \u003cb\u003eSupplement Figure-1\u003c/b\u003e shows that which pathways the up-regulated genes contributed to through the exosone group (early lung cancer \u003cem\u003evs\u003c/em\u003e. normal patient, and advanced lung cancer \u003cem\u003evs\u003c/em\u003e. the normal patient) through GO Enrichment Analysis. KEGG, DO, Reactome Enrichment Analysis showed the correlated pathways consistent with GO Enrichment Analysis. Futhermore, Venn intersection analysis on four sets of differentially expressed genes by regular method showed 44 common differential gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), and 37 upregulated candidate genes were selected to be conducted further analysis. 37 candidate genes, namely, \u003cem\u003eAC093909.1\u003c/em\u003e, \u003cem\u003eBSG\u003c/em\u003e, \u003cem\u003eC9orf16\u003c/em\u003e, \u003cem\u003eCALM3\u003c/em\u003e, \u003cem\u003eCLDN5\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eCMTM5\u003c/em\u003e, \u003cem\u003eCTSA\u003c/em\u003e, \u003cem\u003eDDX11L10\u003c/em\u003e, \u003cem\u003eDDX11L5\u003c/em\u003e, \u003cem\u003eDOK2\u003c/em\u003e, \u003cem\u003eFCER1G\u003c/em\u003e, \u003cem\u003eGPX1\u003c/em\u003e, \u003cem\u003eH2BC12\u003c/em\u003e, \u003cem\u003eH4C9\u003c/em\u003e, \u003cem\u003eHCFC1R1\u003c/em\u003e, \u003cem\u003eHLA-C\u003c/em\u003e, \u003cem\u003eHLA-H\u003c/em\u003e, \u003cem\u003eIFI27L2\u003c/em\u003e, \u003cem\u003eITM2B\u003c/em\u003e, \u003cem\u003eMCEMP1\u003c/em\u003e, \u003cem\u003eMEA1\u003c/em\u003e, \u003cem\u003eNDUFA6\u003c/em\u003e, \u003cem\u003eNDUFAF3\u003c/em\u003e, \u003cem\u003ePF4\u003c/em\u003e, \u003cem\u003ePOLR2E\u003c/em\u003e, \u003cem\u003ePTCRA\u003c/em\u003e, \u003cem\u003eRHOC\u003c/em\u003e, \u003cem\u003eRUFY1\u003c/em\u003e, \u003cem\u003eSAT1\u003c/em\u003e, \u003cem\u003eSH3BGRL3\u003c/em\u003e, \u003cem\u003eSPARC\u003c/em\u003e, \u003cem\u003eSPX\u003c/em\u003e, \u003cem\u003eTMEM106C\u003c/em\u003e, \u003cem\u003eTREML1\u003c/em\u003e, \u003cem\u003eYIF1B\u003c/em\u003e, \u003cem\u003eYWHAZP2\u003c/em\u003e were identified (\u003cb\u003eSupplement\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Venn intersection analysis by log2FoldChange showed no common differential gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). We obtained expression data from the TCGA and GTEx databases to analyze the mRNA levels of 37 potential genes in cancer and normal tissues. The findings revealed a marked increase in \u003cem\u003eTREML1\u003c/em\u003e expression in cancerous tissues compared to normal tissues in both the exosome and platelet groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, \u003cb\u003eSupplement\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Consistently, the clinical and molecular characteristics of \u003cem\u003eTREML1\u003c/em\u003e in lung cancer was validated in the later exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Expression and prognosis profile of TREML1\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows a notable increase in \u003cem\u003eTREML1\u003c/em\u003e expression in lung cancer, specifically in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), compared to their corresponding normal samples. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB illustrates a significant difference in \u003cem\u003eTREML1\u003c/em\u003e expression among various pathological stages, suggesting a strong correlation between \u003cem\u003eTREML1\u003c/em\u003e and the clinical stage of lung cancer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOur findings suggested that increased \u003cem\u003eTREML1\u003c/em\u003e levels were associated with a poor outlook in terms of overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and disease-free survival (DFS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) in lung cancer patients from the TCGA dataset.Immunohistochemistry analysis revealed that \u003cem\u003eTREML1\u003c/em\u003e exhibited decreased levels in lung cancer tissue samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eTREML1 mutation analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, obtained from cBioPortal, shows the genetic changes in \u003cem\u003eTREML1\u003c/em\u003e for samples from patients. The highest alteration frequency of \u003cem\u003eTREML1\u003c/em\u003e occurs among patients with Esophageal adenocarcinoma (EAC), where the primary type of alteration was \u0026lsquo;amplification\u0026rsquo; referring to copy number alteration (CNA). Six types of cancer with genetic mutations showed significant amplification of \u003cem\u003eTREML1\u003c/em\u003e: stomach adenocarcinoma (STAD), ovarian serous cystadenocarcinoma (OVs), diffuse large B-Cell lymphoma (DLBCL), skin cutaneous melanoma (SKCM), cholangiocarcinoma, and lung adenocarcinoma. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB,C, the TCGA cohort displayed multiple mutation sites in \u003cem\u003eTREML1\u003c/em\u003e, highlighting the site with the highest frequency of alteration (N290K) in the V-set within the 3D structure of \u003cem\u003eTREML1\u003c/em\u003e. A single sample from the TCGA repository exhibited a mutation at this common location in a case of LUAC. Nevertheless, the N290K mutation in \u003cem\u003eTREML1\u003c/em\u003e had no impact on the overall survival of patients with LUAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The correlation between clinical characteristic and TREML1 expression\u003c/h2\u003e \u003cp\u003eTo improve comprehension of the relationship between \u003cem\u003eTREML1\u003c/em\u003e expression and clinical characteristics in various types of cancer. Sangerbox 3.0 online tools are utilized to analyze the relationship between important clinical features and \u003cem\u003eTREML1\u003c/em\u003e expression in TCGA dataset. Notable variances in \u003cem\u003eTREML1\u003c/em\u003e expression levels were noted among different groups based on proliferation, invasion, lymph node metastasis, distant metastases, total clinical stage, and gender in patients with LUAD, BRCA, ESCA, STES, KIPAN, STAD, PRAD, HNSC, LUSC, and LIHC. As shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cem\u003eTREML1\u003c/em\u003e expression was associated with through T status, N status, M status, and total clinical stage in LUAD. Morerover, there is lower correlation with LUSC, comparing with the LUAD. Additionally, \u003cem\u003eTREML1\u003c/em\u003e expression significantly higer in the male patients than the female patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eBased on the aforementioned enrichment findings, it is hypothesized that \u003cem\u003eTREML1\u003c/em\u003e plays a role in controlling multiple biological functions including the progression of lung cancer, the regulation of EMT, and metabolic processes. These relevent processes all have been reported to be connected to stem cells. We will delve deeper into the cellular stemness of \u003cem\u003eTREML1\u003c/em\u003e in lung cancer to determine if \u003cem\u003eTREML1\u003c/em\u003e plays a significant regulatory role in stem cells. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE shows the standardized pan-cancer dataset obtained from 37 tumors. In particular, we collected the \u003cem\u003eTREML1\u003c/em\u003e (ENSG00000161911) expression data from every sample and calculated the RNA stemness scores for each tumor using mRNA characteristics. Next, we computed the Pearson correlation coefficients for each type of tumor and found significant positive correlations in 18 tumors, such as LUAD and LUSC, as well as negative correlations in 15 tumors.Thus, our initial results indicate that \u003cem\u003eTREML1\u003c/em\u003e could have a positive impact on the regulation of stem cells in lung cancer, however, additional experimental confirmation is required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Genetic characteristics of TREML1 mutation in NSCLC\u003c/h2\u003e \u003cp\u003eA large cohort of 995 patients with lung cancer and NGS genomic profiling were enrolled.Among these included patients, 508 were lung adenocarcinoma, of which 255 with low expression of \u003cem\u003eTREML1\u003c/em\u003e and 253 with high expression; 487 were lung squamous cell carcinoma, of which 244 with low expression of \u003cem\u003eTREML1\u003c/em\u003e and 243 with high expression.Lollipop map of hotspot mutations in \u003cem\u003eTREML1\u003c/em\u003e protein present that approximately 0.8% \u003cem\u003eTREML1\u003c/em\u003e mutation ocuurred in lung adenocarcinoma, 0.6% \u003cem\u003eTREML1\u003c/em\u003e mutation ocuurred in lung squamous cell carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The genomic profile of \u003cem\u003eTREML1\u003c/em\u003e mutation from the TCGA cohort was different between LUAD and LUSC.The most co-mutation of \u003cem\u003eTREML1\u003c/em\u003e was \u003cem\u003eTTN\u003c/em\u003e (53.7%), and then \u003cem\u003eRYR2\u003c/em\u003e (42.8%), \u003cem\u003eLRP1B\u003c/em\u003e (37.8%), \u003cem\u003eUSH2A\u003c/em\u003e (36.8%), \u003cem\u003eZFHX4\u003c/em\u003e (35.9%), \u003cem\u003eZNF536\u003c/em\u003e (23.5%), \u003cem\u003eCSMD1\u003c/em\u003e (22.8%), \u003cem\u003eCOL11A1\u003c/em\u003e (22.8%)(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) for lung adenocarcinoma. APOB ranked at the top (20.7%) followed by \u003cem\u003eCSMD\u003c/em\u003e (20.4%), \u003cem\u003eCTNNA\u003c/em\u003e (16.1%), \u003cem\u003eKCNH7\u003c/em\u003e (12.9%), \u003cem\u003eDCDC1\u003c/em\u003e (12.4%), \u003cem\u003eCTNND2\u003c/em\u003e (12.1%), \u003cem\u003eADGRL3\u003c/em\u003e (11.8%), \u003cem\u003eUNC79\u003c/em\u003e (10.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe involvement of the TREM-like transcript-1 (TLT-1) factor family in lung cancer is seldom discussed.In this study, we demonstrated that \u003cem\u003eTREML1\u003c/em\u003e expression level was higher in tumors than in normal lungs by combining mRNA sequencings of self-test samples and the public experiments on patients\u0026rsquo; specimens. TLT-1 contains an IgV domain,\u003csup\u003e18\u003c/sup\u003e similar to other soluble immunocheckpoint ligands/receptors,\u003csup\u003e19\u003c/sup\u003e and is one of the most abundant transcripts found in platelets.\u003csup\u003e20\u003c/sup\u003e Our preliminary studies have shown that we can detect the mRNA from platelets while simultaneously detecting exosomes. We discovered upregulated genes that show differential expression in exosomes and platelets, identified a group of 37 genes present in the list of differentially expressed genes for early lung cancer compared to healthy individuals, and for advanced lung cancer compared to healthy individuals in both exosomes and platelets. In line with a prior study \u003csup\u003e21\u003c/sup\u003e that showed in mice with compatible tumors, the interaction between TLT-1 and T cells resulted in the inhibition of CD8 T cells, thus supporting tumor development. It is worth mentioning that the majority of these pathways are connected to the field of tumor immunology in our study. To do so, we analyzed transcriptomic data from two large independent publicly database (TCGA and GTEx) for external verification. Our findings indicated that \u003cem\u003eTREML1\u003c/em\u003e levels may increase in reaction to tumor immunology during the transition from early to late stages of lung cancer. Nevertheless, the role of \u003cem\u003eTREML1\u003c/em\u003e in the advancement of lung cancer remains uncertain. Pathological and functional analyses were conducted to demonstrate the crucial involvement of \u003cem\u003eTREML1\u003c/em\u003e in the advancement of lung cancer. The authenticity of the signature gene \u003cem\u003eTREML1\u003c/em\u003e was confirmed through immunohistochemistry, staining outcomes, and examination of its protein structure in HPA databases.\u003c/p\u003e \u003cp\u003eInitial findings indicated that platelet activity in sepsis identified the receptor, triggering receptor expressed on myeloid cells (TREM)-like transcript-(TLT)-1, as a crucial factor in the advancement of sepsis.\u003csup\u003e22\u003c/sup\u003e Chia-Ming Chang et al\u003csup\u003e23\u003c/sup\u003e revealed a new route in which platelets oppose immune responses to infection via TLT-1, and they have conducted research on how sTLT-1 interacts with and influences the host immune system during sepsis. Additionally, research has shown that \u003cem\u003eTREML1\u003c/em\u003e and \u003cem\u003eTREML2\u003c/em\u003e could increase the risk of Alzheimer's disease by influencing both amyloid-β pathology and neuronal degeneration, potentially serving as neurodegenerative markers.\u003csup\u003e24\u003c/sup\u003e Understanding the genomic characteristics, transcriptomic profile, and survival outlook of \u003cem\u003eTREML1\u003c/em\u003e is crucial for predicting tumorigenesis in lung cancer and developing effective therapeutic strategies. In this study, we thoroughly examined the frequency and genetic variability of \u003cem\u003eTREML1\u003c/em\u003e in human cancer using a resolution for mutant alleles, while also taking into account host factors such as age, TNM stage, tumor stemness, and gender. High expression of \u003cem\u003eTREML1\u003c/em\u003e was found to be a risk factor in the development and advancement of various cancers, including esophageal adenocarcinoma, stomach adenocarcinoma, ovarian serous cystadenocarcinoma, diffuse large B-Cell lymphoma, skin cutaneous melanoma, cholangiocarcinoma, sarcoma, bladder urothelial carcinoma, and others. The highest alteration frequency of \u003cem\u003eTREML1\u003c/em\u003e is N290K in the V-set domain. We further investigate the prognostic predictive ability of \u003cem\u003eTREML1 N290K\u003c/em\u003e by analyzing OS, PFS, DFS, and DSS. The results demonstrated that the \u003cem\u003eTREML1 N290K\u003c/em\u003e woud not accelerate the occurrence of tumors. Obviously, we linked the distinct molecular tracks of \u003cem\u003eTREML1 N290K\u003c/em\u003e lung cancer would not lead to differential clinical outcomes. We have plotted a waterfall plot of co-expression of this \u003cem\u003eTREML1\u003c/em\u003e both in LUAD and LUSC. Analysis of gene mutations revealed that \u003cem\u003eTTN\u003c/em\u003e (53.7%) is the most frequent alteration associated with \u003cem\u003eTREML1\u003c/em\u003e overexpression in LUAD, while \u003cem\u003eAPOB\u003c/em\u003e is the most common alteration in LUSC.\u003c/p\u003e \u003cp\u003eThe study still has some limitations.Because there are not enough clinical cases and limited samples, we were unable to sequence each group coherently, so further exploration may be necessary in a larger sample size to uncover differential mutation or expression patterns. Likewise, this research is limited to a single institution, potentially introducing bias that could be mitigated by including a larger sample size in future studies. Secondly, there were several in-depth mechanism has not been explored and explained clearly. We could not explain what transcription foctor the \u003cem\u003eTREML1\u003c/em\u003e promotes ultimately leading to enhanced stemness. Due to limitations in sample size, we could not unable the meaningful mutations with RNA-sequencing of self-test samples and public database, futhermore, the specific tumor-promoting variants could not be present and then we may not develop specific targeted drugs to the \u003cem\u003eTREML1\u003c/em\u003e in the future.\u003c/p\u003e \u003cp\u003eOur current research provided a more thorough understanding of \u003cem\u003eTREML1\u003c/em\u003e's role in lung cancer development. The results of our study indicate that \u003cem\u003eTREML1\u003c/em\u003e is a suitable target for prognostic and therapeutic markers. Additional research is required to comprehensively grasp how \u003cem\u003eTREML1\u003c/em\u003e interacts with these signaling pathways, which will be the primary focus of our upcoming studies.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, after examining clinical cohort and cellular RNA-seq information, it was determined that elevated \u003cem\u003eTREML1\u003c/em\u003e levels are linked to the development of tumors in lung cancer. Our findings unequivocally identify \u003cem\u003eTREML1\u003c/em\u003e as a potential predictor implicated in the advancement of lung cancer, warranting additional research into the mechanisms through which \u003cem\u003eTREML1\u003c/em\u003e facilitates the progression of early-stage lung cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDan Liu, Jiadi Gan conceived the project and designed the experiments. Wenliang Qiao, Juan Chen, Yongfeng Yang, Wang Hou, Kaixin Lei collected the samples, performed sequencing experiments, and processed the data. Haibo Wang, Guonian Zhu, Jinghong Xian, Zhoufeng Wang performed the experiments. Wenliang Qiao, Jiadi Gan, Dan Liu conceived the study and wrote the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research was provided by the National Natural Science Foundation of China (grant \u0026nbsp; number 92159302), the Science and Technology Project of Sichuan (grant number 2022ZDZX0018), the National Natural Science Foundation of China (grant number 82173182), the Science and Technology Program of Sichuan (grant number 2023NSFSC1939), and the 1.3.5 Project for Disciplines of Excellence at West China Hospital, Sichuan University (grant number ZYJC 21054). The primary research and development initiatives of the Sichuan Provincial Department of Science and Technology assigned to W.L.Q in project 2020YFS0251.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available onrequest from the corresponding author. The information is unavailable to the public because of privacy or ethical limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writers assert that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe West China Hospital Research Ethics Committee and ethics committees approved this study, which did not interfere with clinical management. Informed consent (oral or written) was obtained from study participants according to local requirements, except for cases in which a local committee granted a waiver or exemption. We adhered to the Declaration of Helsinki and Good Clinical Practice guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A, Cancer statistics (2024) CA Cancer J Clin 2024 Jan-Feb 74(1):12\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209\u0026ndash;249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinna JD, Roth JA, Gazdar AF (2002) Focus on lung cancer. Cancer Cell 1(1):49\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrabhakar B, Shende P, Augustine S (2018) Current trends and emerging diagnostic techniques for lung cancer. Biomed Pharmacother 106:1586\u0026ndash;1599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalehjahromi M, Karpinets TV, Sujit SJ et al (2024) Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept. Cell Rep Med 5(3):101463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNa KJ, Kim YT, Goo JM et al (2024) Clinical utility of a CT-based AI prognostic model for segmentectomy in non-small cell lung cancer. Radiology 311(1):e231793\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen K, He Y, Wang W et al (2024) Development of new techniques and clinical applications of liquid biopsy in lung cancer management. Sci Bull (Beijing) 69(10):1556\u0026ndash;1568\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu P, Zhang C, Tang X et al (2024) Pan-cancer characterization of cell-free immune-related miRNA identified as a robust biomarker for cancer diagnosis. Mol Cancer 23(1):31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzegbogu M, Wilkinson E, Reid G et al (2024) Cell-free DNA methylation in the clinical management of lung cancer. Trends Mol Med 30(5):499\u0026ndash;515\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTkach M, Thery C (2016) Communication by extracellular vesicles: where we are and where we need to go. Cell 164(6):1226\u0026ndash;1232\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreethi KA, Selvakumar SC, Ross K et al (2022) Liquid biopsy: Exosomal microRNAs as novel diagnostic and prognostic biomarkers in cancer. Mol Cancer 21(1):54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrashekar DS, Karthikeyan SK, Korla PK et al (2022) UALCAN: an update to the integrated cancer data analysis platform. Neoplasia 25:18\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Z, Kang B, Li C, Chen T, Zhang Z (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1):W556\u0026ndash;W560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsplund A, Edqvist PH, Schwenk JM, Ponten F (2012) Antibodies for profiling the human proteome\u0026mdash;the Human Protein Atlas as a resource for cancer research. Proteomics 12(13):2067\u0026ndash;2077\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Z, Kang B, Li C, Chen T, Zhang Z (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1):W556\u0026ndash;W560\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerami E, Gao J, Dogrusoz U et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401\u0026ndash;404\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao J, Aksoy BA, Dogrusoz U et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269):pl1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBranfield S, Washington AV (2021) The enigmatic nature of the triggering receptor expressed in myeloid cells \u0026ndash;\u0026thinsp;1 (TLT- 1). Platelets 32(6):753\u0026ndash;760\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasak AJ, Maiti S, Hansda A et al (2020) Structural insights into N-terminal IgV domain of BTNL2, a T cell inhibitory molecule, suggests a non-canonical binding interface for its putative receptors. J Mol Biol 432(22):5938\u0026ndash;5950\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenis YA, Tomlinson MG, Garc\u0026iacute;a A et al (2007) A comprehensive proteomics and genomics analysis reveals novel transmembrane proteins in human platelets and mouse megakaryocytes including G6b-B, a novel immunoreceptor tyrosine-based inhibitory motif protein. Mol Cell Proteom 6(3):548\u0026ndash;564\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTyagi T, Jain K, Yarovinsky TO et al (2023) Platelet-derived TLT-1 promotes tumor progression by suppressing CD8\u0026thinsp;+\u0026thinsp;T cells. J Exp Med 220(1):e20212218\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWashington AV, Gibot S, Acevedo I et al (2009) TREM-like transcript-1 protects against inflammation-associated hemorrhage by facilitating platelet aggregation in mice and humans. J Clin Invest 119(6):1489\u0026ndash;1501\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang CM, Cheng KH, Wei TY et al (2023) Soluble TREM-like Transcript-1 Acts as a Damage-Associated Molecular Pattern through the TLR4/MD2 Pathway Contributing to Immune Dysregulation during Sepsis. J Immunol 210(9):1351\u0026ndash;1362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian ML, Ni XN, Li JQ et al (2019) Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative. A candidate regulatory variant at the TREM gene cluster confer Alzheimer's Disease risk by modulating both amyloid-β pathology and neuronal degeneration. Front Neurosci 13:742\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TREML1, mRNA-sequencing, biomarker, prognosis, lung cancer.","lastPublishedDoi":"10.21203/rs.3.rs-4616157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4616157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLung cancer is a major contributor to cancer rates and deaths worldwide. Due to its complexity and variability, lung cancer progresses quickly and has a grim outlook, making early and precise diagnosis imperative. Despite numerous clinical methods available to aid doctors in detecting lung cancer, there is still a need for a non-invasive biomarker for cancer development.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examine the levels of \u003cem\u003eTREML1\u003c/em\u003e mRNA and protein expression in exosomes derived from tumors in both normal and cancerous lung tissues of humans, utilizing information from TCGA, GTEx, HPA databases, as well as samples obtained from clinical settings. Validation experiments were performed on tissue microarrays obtained from lung cancer samples. We examined targeted next-generation sequencing data from the TCGA database to gain insight into the frequency of \u003cem\u003eTREML1\u003c/em\u003e mutations and the collection of genes that are co-altered in tumors with \u003cem\u003eTREML1\u003c/em\u003e mutations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings reveal that \u003cem\u003eTREML1\u003c/em\u003e is highly expressed in lung cancer, and could be one valueable predictor which may be applied in clinic in the future. Analysis of survival data from the TCGA and GTEx database suggests that high levels of \u003cem\u003eTREML1\u003c/em\u003e expression are associated with poor clinical prognosis in lung cancer. Analysis of gene mutations revealed that \u003cem\u003eTTN\u003c/em\u003e (53.7%) is the most frequent alteration associated with \u003cem\u003eTREML1\u003c/em\u003e overexpression in LUAD, while \u003cem\u003eAPOB\u003c/em\u003e is the most common alteration in LUSC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIt can be concluded that \u003cem\u003eTREML1\u003c/em\u003e is a suitable target for prognosis and treatment markers. Additional research is required to comprehensively grasp how \u003cem\u003eTREML1\u003c/em\u003e interacts with these signaling pathways, which will be the primary focus of our upcoming studies.\u003c/p\u003e","manuscriptTitle":"Tumor-originated exosomal TREML1 is a novel predictive biomarker for tumorigenesis in lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 11:44:15","doi":"10.21203/rs.3.rs-4616157/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c9ba39-8ada-443f-ae0b-4f9ce999f4a8","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T19:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-12 11:44:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4616157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4616157","identity":"rs-4616157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}