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

doi:10.21203/rs.3.rs-4616157/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4616157/v1

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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.

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 colleagues⁸ 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.

2.1 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.

3.1 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. 1A-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. 1E), 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. 1F). 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. 1G, 1H, 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 2A 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 2B 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. 2C) and disease-free survival (DFS) (Fig. 2D) in lung cancer patients from the TCGA dataset.Immunohistochemistry analysis revealed that TREML1 exhibited decreased levels in lung cancer tissue samples (Fig. 2E).

3.3 TREML1 mutation analysis

Figure 3A, 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. 3B,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. 3D).

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. 4A-4D, 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. 4F).

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 4E 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. 5A). 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. 5B) 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. 5C).

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 al²³ 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.

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.

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.

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

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1 Introduction

2 Materials and methods

2.1 Sample acquisition

2.2 mRNA-sequencing data processing

2.3 Differentially expressed genes analysis

2.4 TREML1 expression profile data analysis

2.5 The human protein atlas

2.6 Survival analysis

2.7 Genetic mutation analysis

2.8 Experimental validation

2.9 Statistical analysis

3 Results

3.1 RNA derived from exosome and platelet have different gene expression profiles

3.2 Expression and prognosis profile of TREML1

3.3 TREML1 mutation analysis

3.4 The correlation between clinical characteristic and TREML1 expression

3.5 Genetic characteristics of TREML1 mutation in NSCLC

4 Discussion

5 Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1