Conventional serum inflammatory mediators IL-17 combined with exosomal 5'tRF-GlyCCC and i- tRF-AlaCGC are evaluated as a novel biomarker for the early diagnosis of non-small cell lung cancer

Conventional serum inflammatory mediators IL-17 combined with exosomal 5'tRF-GlyCCC and i- tRF-AlaCGC are evaluated as a novel biomarker for the early diagnosis of non-small cell lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Conventional serum inflammatory mediators IL-17 combined with exosomal 5'tRF-GlyCCC and i- tRF-AlaCGC are evaluated as a novel biomarker for the early diagnosis of non-small cell lung cancer Lei Duan, Yanyan Sang, Lijuan Qi, Jiefei Peng, Qiang Feng, Zhijun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9067483/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background IL-17, an inflammatory cytokine secreted by Th17 cells, plays a pivotal role in tumor immunity and inflammation. Among its subtypes, IL-17A functions as the principal effector. Neutrophil-to-lymphocyte ratio (N/L, NLR) serves as a routine clinical indicator for infection screening, and elevated granulocytes in tumor tissues reflect tumorigenic activity. This study investigated the combined diagnostic and prognostic potential of serum exosomal 5'tRF-GlyCCC and i-tRF-AlaCGC in relation to IL-17 and NLR in non-small cell lung cancer (NSCLC). Methods Bioinformatic analyses explored the biological functions of 5'tRF-GlyCCC and IL-17A using TCGA data. Serum IL-17 levels were quantified by flow cytometry, and NLR was calculated from routine blood tests. Exosomes were isolated from serum and characterized via transmission electron microscopy, particle size analysis, and Western blotting. Differentially expressed tRFs were identified by microarray and validated by qPCR in 242 NSCLC patients and 201 healthy controls. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Results TCGA analysis showed significant differential expression of 5'tRF-GlyCCC and IL17A in cancer patients, with low 5'tRF-GlyCCC expression associated with poor prognosis. NSCLC patients exhibited elevated levels of IL-17 and NLR. Both 5'tRF-GlyCCC and i-tRF-AlaCGC were markedly downregulated in NSCLC (AUC: 0.669 and 0.673) and early-stage disease (AUC: 0.640 and 0.649). The combined AUC for diagnosing NSCLC using all four predictors was 0.926, and for early-stage diagnosis it was 0.901. Abnormally expression of these predictors correlated with poor patient prognosis. Conclusion IL-17 and NLR, combined with serum exosomal 5'tRF-GlyCCC and i-tRF-AlaCGC, show strong diagnostic and prognostic potential for early detection of NSCLC. IL17 N/L 5'tRF-GlyCCC i-tRF-AlaCGC Exosomes AUC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Lung cancer remains a critical global health challenge, with persistently high incidence and mortality rates. Notably, non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases in Asia [ 1 ]. NSCLC is classified into three primary histological subtypes: adenocarcinoma (originating from mucinous gland cells), squamous cell carcinoma (arising from the squamous epithelium of the airways), and large cell carcinoma. Although rare, adenosquamous carcinoma (a mixture of squamous cell carcinoma and adenocarcinoma) and sarcomatoid carcinoma (a mixture of carcinoma and sarcoma) also exist. The incidence of non-small cell lung cancer differs significantly between men and women. The harmful habit of smoking among men exacerbates lung cancer rates, particularly as smoking increasingly affects younger age groups. Reports indicate that since 2020, smokers account for 20% of the global population, with 15.4% of smokers falling within the 15–24 age bracket [ 2 , 3 ]. Early diagnosis and timely intervention are pivotal to reducing NSCLC mortality. In clinical practice, accurate identification of predictive biomarkers for early diagnosis can provide patients with more treatment opportunities and improved outcomes. tRNA-derived small RNAs (tDRs) are fragments generated from precursor or mature tRNAs, typically ranging from 14 to 50 nucleotides in length. They represent a novel class of small non-coding RNAs (ncRNAs) produced through enzymatic cleavage of tRNAs. Based on their biogenesis sites, tDRs are broadly classified into two categories: tRNA halves (tiRNAs), longer fragments (30–50 nt) generated by cleavage in the anticodon loop under stress conditions, and tRNA-derived small RNA fragments (tRFs), shorter fragments (14–30 nt) produced by processing at other tRNA regions (e.g., 5'-tRFs, 3'tRFs, and i-tRFs) [ 4 , 5 ]. The biogenesis of tRFs is not a random process resulting from tRNA degradation but is instead controlled by a highly conserved and precise site-specific cleavage mechanism [ 6 ]. Depending on the cleavage position, tRFs can be classified into 5′-tRFs, 3′-tRFs, and intermediate tRFs (i-tRFs). 5′-tRFs are derived from the 5′ end of mature tRNAs through cleavage at the D-loop. 3′-tRFs are formed by specific cleavage at the 3′ end of mature tRNAs in the T-loop region, whereas i-tRFs are generated from cleavage of the 3′-trailer sequence of precursor tRNAs. As a representative class of non-coding RNAs, tRFs exhibit diverse biological functions similar to microRNAs, particularly in transcriptional and post-transcriptional regulation [ 7 ]. They play important roles in tumorigenesis, stem cell biology, and stress responses [ 8 – 10 ]. Exosomes are membrane-bound vesicles formed through the inward budding of cellular membranes, serving as crucial mediators of intercellular communication. These nanoscale particles are abundantly present in blood and various other bodily fluids, carrying biologically important information from their host cells. In cancer research, exosomes isolated from patient serum have demonstrated significant investigative value [ 11 – 13 ].As essential delivery vehicles, exosomes are extensively studied for their applications in drug transport and cancer therapeutics [ 14 , 15 ]. In novel clinical diagnostics, the molecular cargo contained within exosomes enables real-time monitoring of tumor development and accurate assessment of disease progression. This property enables clinicians to access reliable biomarkers for timely cancer diagnosis and informed treatment decision-making. IL-17 is an effector cytokine produced by Th17 cells and serves as a key inflammatory mediator in various diseases, including allergic reactions, autoimmune disorders, asthma, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis [ 16 – 18 ]. It plays a significant regulatory and promotive role in pulmonary pathologies by mediating inflammatory responses. Evidence suggests that IL-17A, both within the tumor microenvironment and circulating in the blood, correlates with disease progression in lung cancer patients [ 19 , 20 ]. Studies have linked IL-17 to multiple pathological conditions such as chronic inflammation, cancer, and autoimmune diseases [ 21 ], In lung cancer metastasis, IL-17A and γδ T cells play crucial roles, and anti-IL-17 antibody therapy has shown potential in suppressing lung cancer cell growth [ 22 ]. These findings underscore the therapeutic significance of IL-17 in lung cancer, although its diagnostic value for early detection warrants further evaluation. The tumorigenic neutrophil-to-lymphocyte ratio (N/L, NLR), commonly used to assess infection status in immunocompromised patients, has been associated with tumor-promoting granulocytes in breast cancer. This raises an important research question: Could NLR serve as an early predictive marker for NSCLC? The diagnostic efficiency of combining serum exosomal tRF RNAs with IL-17 for NSCLC remains unknown. Our study found that 5′tRF-GlyCCC and i-tRF-AlaCGC were downregulated in the serum of NSCLC patients. The primary focus of this research is to evaluate the diagnostic value of these two tRFs, particularly for the early detection of NSCLC, and to assess the combined diagnostic efficiency when integrated with NLR and IL-17 indicators. Materials and Methods Sample collection and data statistics Samples from 241 NSCLC patients and 201 healthy adult controls were collected between March and August 2021 at Tai'an City Central Hospital. All NSCLC blood samples were obtained before treatment, while the healthy control samples were collected from individuals undergoing routine physical examinations. The expression levels of different tRF RNAs in serum, along with detailed clinical information of lung cancer patients, including gender, age, cancer type, lifestyle factors, and tumor metastasis, were compared with those of healthy controls. Approximately 3 mL of heparin-anticoagulated blood was collected in tubes containing separator gel and centrifuged at 4°C and 1000 × g for 10 min to obtain serum samples, which were then stored at − 80°C for subsequent experiments. Data Preparation Data for lung adenocarcinoma and lung squamous cell carcinoma were downloaded from the TCGA database ( https://portal.gdc.cancer.gov ) to analyze the relationship between prognostic factors and patient outcomes. The infiltration levels of various immune cells in tumor patients, including B cells, T cells, and dendritic cells (DCs), were reassessed based on gene expression profiles. Using R software (version 3.6.4), expression differences between normal and tumor samples within each tumor type were calculated. The unpaired Wilcoxon rank-sum test and paired Wilcoxon signed-rank test were applied to evaluate the statistical significance of these differences. Extraction of exosomes and performance testing Serum collected from NSCLC patients and healthy controls was thawed on ice and centrifuged at 10,000×g, 4°C for 30 min to retain the supernatant and remove the precipitate. The supernatant was then centrifuged at 100,000×g, 4°C for 2 h to remove residual debris, followed by another centrifugation at 120,000×g, 4°C for 2 h to isolate exosomes (Type 50.4 Ti Rotor; Beckman Coulter), which is highly efficient for exosome extraction. The extraction method was performed according to previously published protocols [ 23 , 24 ]. The size and distribution of exosomes were determined using a particle size analyzer (qNano, New Zealand). Fifteen microliters of PBS-washed exosome samples were added to a copper grid. After natural drying, the physical morphology of the exosomes was observed with a FEI Tecnai T20e transmission electron microscope (FEI Company, USA). Western blot analysis was performed to identify exosomal outer membrane proteins using a PVDF membrane (Millipore, Billerica, MA, USA). According to the literature [ 25 , 26 ], three proteins highly expressed on the surface of exosomes were selected for detection: TSG101 (ProteinTech, USA, 1:800), CD81 (ProteinTech, USA, 1:1000), and CD9 (ProteinTech, USA, 1:800). Rabbit anti-exosome antibodies (Abmart, 1:5000) were used, and the membranes were incubated at 37°C for 1 h to visualize the protein bands. RNA extraction and reverse transcription The extracted exosomes were resuspended in PBS, and 1 mL of TRIzol was added to lyse the exosomes for RNA extraction. The extraction method followed the conventional lysis protocol, and the extracted RNA was subsequently reverse transcribed into cDNA for the next qPCR procedure (TaKaRa Bio, Nojihigashi, Kusatsu, Japan). The resulting cDNA was stable and suitable for storage, typically kept at − 20°C. Quantitative PCR (qPCR) assays were then performed according to the following steps. qPCR qPCR was performed to detect the expression of tRF RNA in the serum of healthy controls and patients with NSCLC. U6 was used as the internal reference (F: 5'-CTCGCTTCGGCAGCACA-3', R: 5'-AACGCTTCACGAATTTGCGT-3′). Statistical analysis was conducted based on the measured ΔCT values, and all experiments were independently repeated three times (p < 0.001). Determination of IL17 and NLR Sera collected from healthy controls and NSCLC patients were centrifuged at 3000 rpm for 3 min to remove the supernatant. The expression of IL-17 (Qingdao Risikeer Biotechnology Co., Ltd.) in the sera of procoagulant samples was determined using flow cytometry (NovoCyte D20, ACEA Biosciences). The NLR was measured in the sera of procoagulant samples. The test samples were anticoagulated with EDTA, and the numbers of granulocytes and lymphocytes were determined separately using a hematology analyzer (XN-10 [B4], Mindray). The NLR was then calculated based on these values. Immunohistochemistry Tissue specimens from patients with NSCLC were sectioned into paraffin-embedded slices with a thickness of 5 µm. These sections underwent a series of procedures, including dewaxing and hydration, antigen retrieval, incubation with primary antibodies against IL-17A (Bioworld, China, 1:150) and CCL20 (Abmart, China, 1:200), incubation with secondary antibodies, chromogenic development, hematoxylin staining, and sealing with coverslips. The results were obtained by scanning the sections under a microscope. Both cancerous and adjacent paracancerous tissues were obtained from the same patient specimens. Statistical analysis We first analyzed the qPCR data to determine whether they conformed to a normal distribution. The t-test was used for normally distributed data, and the Mann–Whitney unpaired test was applied for non-normally distributed data. Differences between the analyzed measurements were considered statistically significant at p < 0.05. All data were statistically analyzed using GraphPad Prism 9.5 (GraphPad Software, Inc., CA, USA). The area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of candidate predictors using SPSS 22.0 (Ehningen, Germany). Result Analysis of Predictive Factor Expression in the TCGA Database Multidimensional chart analysis revealed the expression characteristics and clinical correlations of the target genes 5′tRF-GlyCCC (ENSG00000253603) and IL17A in lung cancer (LUAD, LUSC). 5′tRF-GlyCCC exhibited statistically significant downregulation in NSCLC (Figs. 1 A–C). Analysis indicated that low 5′tRF-GlyCCC expression correlated with poor prognosis, showing statistical significance in LUAD. The relationship between 5′tRF-GlyCCC expression and tumor stage was further analyzed (Fig. 1 D). Analysis of the TCGA database revealed downregulation of IL17A in both LUAD and LUSC (Fig. 1 E). High IL17A expression was associated with poor prognosis in early-stage patients (Figs. 1 F-G). Figure 1 H illustrates the relationship between IL-17A expression and tumor stage, showing significantly higher levels of IL-17A in patients with advanced-stage tumors. Heatmap of Correlation Between Expression Levels of 5'tRF-GlyCCC and IL17A with Immune Cells in Non-Small Cell Lung Cancer This study employed visual heatmap analysis to reveal the association patterns between 5′tRF-GlyCCC and IL17A expression and multiple immune cells within the tumor immune microenvironment. The purple spectrum indicates the statistical significance of correlations (darker purple represents lower p-values), while the red–blue spectrum denotes the direction and strength of correlation coefficients (red indicates a positive correlation and blue indicates a negative correlation) (Fig. 2 A). Specifically, statistically significant correlations between 5′tRF-GlyCCC and plasma cells, CD4⁺ memory T cells, and T-follicular helper cells are indicated by asterisks (*). Different levels of correlation were observed across varying degrees of cellular infiltration. Figure 2 B evaluates correlations with the Immune Score. In LUSC patients, 5′tRF-GlyCCC expression shows a statistically significant negative correlation with immune cell types. Figure 2 C demonstrates that IL17A exhibits statistically significant correlations with B cells, CD8⁺ T cells, Treg cells, and other immune cells, also indicated by asterisks (*). It further reveals varying degrees of correlation with immune cell infiltration levels. Figure 2 D displays a significant positive correlation between IL17A expression and the Immune Score, with statistically significant differences. Identification of Serum Exosomes Exosome samples extracted via ultracentrifugation underwent identification and analysis. Transmission electron microscopy (Figs. 3 A-B) revealed spherical exosomes with diameters predominantly clustered around 100 nm, exhibiting a normal size distribution. Further characterization of the exosomal vesicles was performed via Western blot analysis, which confirmed the presence of characteristic exosomal surface proteins. These proteins displayed expression patterns distinct from those observed on the cellular membrane, with exosomes showing high expression of TSG101, CD9, and CD81 (Fig. 3 C). These results confirm the successful isolation and identification of exosomes. High-throughput gene chip sequencing identified 24 tRF RNAs with characteristic expression profiles (Table 1), comprising 12 significantly upregulated and 12 downregulated transcripts. Sequencing samples included three healthy control groups, three non-metastatic NSCLC groups, and three metastatic NSCLC groups. Subsequent validation experiments were conducted using qPCR analysis. Expression of 5’tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL17 in Serum from Non-Small Cell Lung Cancer Patients and Early-Stage Patients Serum exosome-derived tRF RNA was subjected to reverse transcription and real-time qPCR. The results revealed that both 5′tRF-GlyCCC and i-tRF-AlaCGC expression levels were significantly downregulated in serum exosomes from 242 NSCLC patients compared with 201 healthy controls (Figs. 4 A-B). The NLR was significantly elevated in the serum of NSCLC patients, indicating increased granulocyte activity (Fig. 4 C). The inflammatory cytokine IL-17 was also markedly overexpressed in serum samples (Fig. 4 D), showing a statistically significant difference (P < 0.0001). In serum exosomes from 93 early-stage NSCLC patients, 5′tRF-GlyCCC and i-tRF-AlaCGC expression levels were also significantly downregulated (P < 0.05; Figs. 4 E-F). Similarly, the tumorigenic N/L ratio was substantially higher in NSCLC serum samples (P < 0.005; Fig. 4 G). IL-17 expression was also significantly increased in serum (P < 0.0001; Fig. 4 H). Relationship Between Predictors and Clinical Pathological Grading Clinical data from patients were collected for pathological analysis. 5′tRF-GlyCCC and i-tRF-AlaCGC expression levels were statistically analyzed in relation to individual patient characteristics. The data indicated that 5′tRF-GlyCCC and i-tRF-AlaCGC showed no statistically significant differences across patient gender, age, pathological type, lifestyle habits, or tumor metastasis status (Table 2 ). Survival duration in NSCLC patients was correlated with tumor grading, emphasizing the importance of early diagnosis as a research priority. Our screening revealed no statistically significant association between 5′tRF-GlyCCC or i-tRF-AlaCGC expression and primary tumor T stage (Figs. 5 A, D). Similarly, no significant correlation was observed between the expression of these genes and lymph node metastasis (P > 0.05; Figs. 5 B, E). However, 5′tRF-GlyCCC exhibited a statistically significant difference (P < 0.05) between stage I and stage III tumors but showed no significance between other stages (Fig. 5 C). i-tRF-AlaCGC showed no statistical significance across clinical stages in NSCLC patients. Efficacy of Predictors in Diagnosing NSCLC The diagnostic accuracy of 5′tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17 as diagnostic markers for NSCLC was evaluated using receiver operating characteristic (ROC) curve analysis and quantified by the area under the curve (AUC). The diagnostic efficiency of 5′tRF-GlyCCC was 0.669 (Fig. 6 A), while that of i-tRF-AlaCGC was 0.673 (Fig. 6 B). The tumorigenic N/L ratio also demonstrated high diagnostic efficiency with an AUC of 0.745, and IL-17 achieved the highest single-marker efficiency at 0.902 (Figs. 6 C-D). Combined analysis of 5′tRF-GlyCCC and i-tRF-AlaCGC improved diagnostic efficiency to 0.687 (95% confidence interval [CI]: 0.636–0.737; specificity 0.442; accuracy 0.884) (Fig. 6 E). When both tRF RNAs were combined with IL-17, diagnostic performance increased significantly, yielding an AUC of 0.905, while combining the two tRF RNAs with tumorigenic N/L also produced a high AUC of 0.806 (Figs. 6 F-G). The combination of all four predictive markers, 5′tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17, demonstrated excellent diagnostic efficiency for NSCLC, achieving an AUC of 0.926 (95% CI: 0.901–0.951; specificity 0.899; sensitivity 0.822) (Fig. 6 H). Survival analysis of all NSCLC cases revealed that low expression levels of 5′tRF-GlyCCC and i-tRF-AlaCGC were associated with poorer prognosis, although the differences were not statistically significant (Figures S1 A-B). Conversely, high expression levels of IL-17 and N/L were significantly correlated with reduced survival rates (Figures S1 C-D). Four predictors for early diagnosis of non-small cell lung cancer Early diagnosis of NSCLC remains a primary focus of research. Using SPSS statistical analysis, the early diagnostic efficiencies (AUC values) of 5′tRF-GlyCCC and i-tRF-AlaCGC were 0.640 and 0.649, respectively (Figs. 7 A-B). The early diagnostic efficiency of N/L was also relatively high, with an AUC of 0.578, whereas IL-17 exhibited a notably strong diagnostic performance, achieving an AUC of 0.901 (Figs. 7 C-D).When combined, 5′tRF-GlyCCC and i-tRF-AlaCGC improved early diagnostic efficiency to 0.654. The combination of both tRF RNAs with IL-17 further enhanced early diagnostic value, reaching an AUC of 0.911. Additionally, the combination of the two tRF RNAs with N/L yielded a high AUC of 0.687 (Figs. 7 E-F). The combined use of all four predictors, 5′tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17, demonstrated the highest early diagnostic efficiency for NSCLC, achieving an AUC of 0.919 (95% confidence interval [CI]: 0.626–0.734; specificity 0.915; accuracy 0.806) (Fig. 7 G). Immunohistochemistry Aids Diagnosis of NSCLC Immunohistochemical staining provided direct histological validation, demonstrating that IL-17A is significantly overexpressed in NSCLC tissues. This overexpression was particularly prominent in lymphocyte-rich regions (red-boxed areas), where cytoplasmic IL-17A staining was markedly elevated. Enhanced cytoplasmic IL-17A expression was also evident within tumor tissue (Fig. 8 A). CCL20, a pro-inflammatory cytokine abundantly secreted by granulocytes, was likewise highly expressed in NSCLC tissue specimens, displaying pronounced cytoplasmic localization and strong staining intensity (Fig. 8 B). Discussion The incidence and mortality rates of lung cancer remain high, and its etiology is complex [ 27 ].In particular, the lack of personal health awareness among patients leads to low diagnosis and cure rates [ 28 ]. The non-invasive diagnostic rate for NSCLC remains low, especially for early-stage detection, which is a major factor leading to poor survival outcomes in NSCLC patients [ 29 ]. In this study, we combined tRF RNA markers with the inflammatory cytokine IL-17 to improve the diagnostic accuracy for NSCLC, while also exploring the elevated expression of inflammatory cells in affected patients. Previous studies have demonstrated that IL-17 expression increases with tumor progression, whereas the number of Th17 cell infiltrates decreases in patients with advanced-stage disease [ 19 ]. Using the TCGA database, we analyzed the expression of predictive factors 5′tRF-GlyCCC, i-tRF-AlaCGC, and IL-17A in serum samples from NSCLC patients. However, data related to i-tRF-AlaCGC were not available in the database, so only 5′tRF-GlyCCC and IL-17A were analyzed. Our findings showed that 5′tRF-GlyCCC expression was not correlated with tumor stage. Previous research has shown that IL-17A and its downstream signaling molecules, including ERK1/2, NF-κB, MMPs, and VEGF, represent potential molecular targets for cancer prevention and therapy, particularly in breast cancer [ 30 ]. Further investigations by Beibei Xu, Esra A. Akbay, et al. demonstrated that elevated IL-17A expression in lung cancer promotes tumor proliferation through IL-6 and tumor-associated neutrophils. Their analyses also revealed that IL-17A mediates resistance to programmed cell death protein 1 (PD-1) blockade therapy [ 31 , 32 ]. In our study, oncogenic neutrophils and granulocytes were significantly elevated in the serum of NSCLC patients (Fig. 3 C), and their increased expression was strongly associated with poor prognosis. Therefore, we incorporated the NLR, a routinely available hematological index, into our NSCLC diagnostic model. C-C motif chemokine ligand 20 (CCL20) has been linked to poor prognosis in breast cancer patients. The CCL20–CCR6 axis directly facilitates tumor progression by promoting cancer cell proliferation and migration, and indirectly remodels the tumor microenvironment through the regulation of immune cell [ 33 ]. Tumor-derived CCL20, activated by Fusobacterium nucleatum (Fn), enhances colorectal cancer metastasis and contributes to tumor microenvironment reprogramming [ 34 ]. Additionally, the overexpression of circSMARCC1 promotes CD163 expression in macrophages through the CCL20–CCR6 pathway, leading to tumor-associated macrophage infiltration and M2 polarization, which drives prostate cancer progression [ 35 ]. Research by Dan Wang et al. further suggests that serum CCL20 combined with IL-17A serves as an early diagnostic and prognostic biomarker for human colorectal cancer. Based on these findings, our study highlights the combined diagnostic value of IL-17A, tRF RNA, and the N/L ratio, three biologically and clinically relevant markers, as promising predictive indicators for the early detection of NSCLC. Serum exosomes contain crucial genetic information derived from patients, and analyzing their molecular components is essential for improving cancer diagnostic accuracy. Exosomes carry various biomolecules that facilitate intercellular communication and play vital roles in tumor initiation, progression, and metastasis, key areas of focus in this study [ 36 , 37 ]. As one of the representatives of non-coding RNAs, tRF RNA participated in gene regulation and modification in vivo, particularly in post-transcriptional regulation [ 38 , 39 ]. In cancer, tRF RNAs modulate intracellular signaling, thereby influencing the proliferation of tumor cells. For instance, tRF3008A suppresses colorectal cancer development by inhibiting endogenous FOXK1 expression [ 40 ]. Similarly, 3′tRF-AlaAGC promotes malignant activity in breast cancer cells and induces M2 macrophage polarization both in vitro and in vivo, with elevated M2 macrophage expression correlating with lymph node metastasis and deeper tumor invasion [ 41 ]. Furthermore, our prior research identified a novel anti-cancer mechanism of 25(OH)D, demonstrating the role of tRF-1-Ser in breast cancer progression and proposing it as a potential therapeutic target [ 42 ]. Both 5′tRF-GlyCCC and i-tRF-AlaCGC are tRNA-derived fragments, and we report for the first time their significantly reduced expression in the serum of patients with NSCLC. Validation using gene chip sequencing confirmed the diagnostic relevance of these findings. The combined diagnostic AUC of these two markers for NSCLC was 0.687 (Fig. 6 E), while for early-stage NSCLC it was 0.654. Although their individual diagnostic efficiencies were moderate, combining them with inflammatory markers markedly enhanced diagnostic performance. In particular, tumor-associated neutrophil infiltration significantly increased diagnostic accuracy for NSCLC. Future studies will focus on elucidating the molecular signaling mechanisms that drive NSCLC metastasis, aiming to identify new therapeutic targets. Immunohistochemical staining revealed strong overexpression of CCL20 in NSCLC tissues. As a marker of tumor-promoting neutrophil infiltration, CCL20 serves as a key indicator for predicting and validating the progression of NSCLC. Targeting the IL-17 cytokine or its associated pathways may represent a promising therapeutic strategy. However, IL-17 alone is insufficient to fully characterize cancer progression. Th17 cells are the primary source of IL-17A (IL-17), while γδ T cells are important initiators of inflammation. Accordingly, our immunohistochemical validation focused on IL-17A expression [ 43 ]. To achieve a more comprehensive evaluation, we incorporated non-coding tRF RNAs into the NSCLC diagnostic assessment. The diagnostic performance was substantially enhanced when IL-17 was combined with the NLR, resulting in an AUC of 0.926. In contrast, the combination of tRF RNA with NLR alone yielded an AUC of 0.806, indicating notable improvement. For the first time, we identified and applied 5’tRF-GlyCCC, i-tRF-AlaCGC, IL-17, and NLR as integrated biomarkers for the early diagnosis of NSCLC, demonstrating their effectiveness as a cost-efficient and highly accurate diagnostic approach. Conclusion Their diagnostic efficacy is markedly enhanced when combined with IL-17 and the neutrophil-to-lymphocyte (N/L) ratio. 5′tRF-GlyCCC and i-tRF-AlaCGC represent newly identified biomarkers with strong potential for the early diagnosis of NSCLC. Histological analysis further validates the high diagnostic value of IL-17A and CCL20. Together, these low-cost and easily accessible biomarkers hold promise as effective tools for early clinical detection and risk assessment in NSCLC. Declarations Acknowledgment The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding This study was financially supported by The Shandong Provincial Medical and Health Science and Technology Projects (202511000079) and Tai'an City Science and Technology Innovation Development Project (No.2024NS293, No.2023NS419). Authors’ contributions All authors participated in the design, interpretation of the studies and analysis of the data and review of the manuscript; LD and ZJZ designed the experiments; YYS and LD performed the experiments and analyzed the data; LJQ and YYS reviewed and analyzed clinical medical records; JFP wrote the original manuscript, which was reviewed by QF and ZJZ. The final manuscript has the approval of all authors. Conflict of Interest Lei Duan, Yanyan Sang, Lijuan Qi, Jiefei Peng, Qiang Feng and Zhijun Zhang declare that they have no competing interests. Data availability The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Ethics approval This study received ethical approval from the Ethics Committee of The Affiliated Taian City Central Hospital of Qingdao University, with the ethics approval numbe: 20230510. All subjects gave their informed consent in accordance with the Declaration of Helsinki. Consent to participate The patients consented to use their tissue, clinical, and pathological information for the experimental research, and all signed an informed consent form. The specimens we used were discarded samples from the clinical laboratory, so no clinical experiments were involved. Clinical trial is not applicable. Consent for publication Our raw data, and manuscript did not contain any individual details, images, or videos. The authors used to number the cases to maintain confidentiality of patient data. 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Tables Table 1: tRFRNA expression profiling (12 up-regulated and 12down-regulated) tRFRNA Fold change Description P value tRFRNA Fold change Description P value 5'tRF-LySCTT 5.2820 Up 0.0352* 3'tiRNA-ArgCCT 0.4563 Down 0.0252* 5'tRF-ProAGG 2.1272 Up 0.0096** 3'tRF-ArgTCT 0.4862 Down 0.0091** 3'tRF-ArgACG 1.5124 Up 0.0406* 3'tRF-TyrGTA 0.4601 Down 0.0020** i-tRF-LeuCAA 1.6801 Up 0.0298* 5'tRF-GlyCCC 0.3569 Down 0.0127* i-tRF-SerGCT 2.4099 Up 0.0068** 3'tiRNA-SerCGA 0.3619 Down 0.0048** 5'tRF-ASnATT 2.4070 Up 0.0192* 3'tiRNA-MetCAT 0.2757 Down 0.0075** 5'tRF-IleAAT 2.3299 Up 0.0062** i-tRF-TrpCCA 0.4491 Down 0.0007*** 5'tRF-SerAGA 2.4687 Up 0.0018** 5'tiRNA-HisGTG 0.4754 Down 0.0080** 5'tRF-ThrTGT 2.2617 Up 0.0040** 3'tRF-ValTAC 0.4447 Down 0.0148* 5'tRF-CysGCA 3.1212 Up 0.0289* 3'tRF-AlaAGC 0.4406 Down 0.0104* 5'tRF-AlaTGC 1.8001 Up 0.0435* 3'tiRNA-LeuAAG 0.4697 Down 0.0081** 5'tRF-ValAAC 2.5470 Up 0.0064** i-tRF-AlaCGC 0.6516 Down 0.0281* *P<0.05; **P<0.01; ***P<0.001 Table 2. Characteristics of NSCLC patients for differentially expressed exosomes 5’tRF-1-GlyCCC and i-tRF-AlaCGC Characteristic No. cases 5’tRF-1-GlyCCC No. cases i-tRF-AlaCGC Median with interquartile range P-value Median with interquartile range P-value Age (year) ≤62 111 -0.9713（-2.652~0.2039） 0.0676 113 -2.315 （-4.349~-0.7397） 0.3449 >62 126 -0.4646（-2.124~0.7534） 124 -2.255（-3.324~-1.081） Gender Male 134 -0.7429（-2.327~0.6867） 0.3962 134 -2.329（-3.772~-0.9620） 0.5520 Female 104 -0.8707（-2.324~0.4413） 104 -2.160（-3.670~-0.9668） Smoking Smoker 100 -0.5515（-2.308~0.8004） 0.2254 100 -2.321（-3.756~-0.8343） 0.7531 history non-smoker 138 -0.8911（-2.361~0.2908） 138 -2.240（-3.67 8~-1.064） Drinking Drinker 85 -0.5270（-2.281~0.7042） 0.2556 85 -2.383（-3.652~-0.9359） 0.9039 history non-drinker 153 -0.9014（-2.439~-0.5022） 153 -2.160（-3.699~-1.018） Lymph node metastasis Yes 122 -0.9364（-2.476~0.2069） 0.1812 124 -2.276（-3.796~-1.016） 0.7711 No 118 -0.5773（-2.170~0.7626） 117 -2.293（-3.481~-0.7899） Distant metastasis Yes 166 -0.8377（-2.386~0.3985） 0.2463 167 -2.271（-3.661~-0.7750） 0.9934 No 65 -0.3370（-2.071~0.8318） 65 -2.048（-3.477~-0.9911） NSCLC, non-small cell lung cancer Additional Declarations No competing interests reported. Supplementary Files Supply1.jpg Figure S1. Survival analysis based on four predictive factors in NSCLC patients: 5′tRF-GlyCCC (A), i-tRF-AlaCGC (B), Neutrophil-to-Lymphocyte ratio (NLR) (C), and IL-17 (D). Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 19 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 13 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9067483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610961396,"identity":"95fbe39e-0199-4d73-be42-e775e2952919","order_by":0,"name":"Lei Duan","email":"","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Duan","suffix":""},{"id":610961397,"identity":"d3e57fec-d879-4b93-b75b-f6c52ce718b9","order_by":1,"name":"Yanyan Sang","email":"","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Sang","suffix":""},{"id":610961399,"identity":"a3fefa8f-fa8f-4679-bb46-b1b9f47f26fd","order_by":2,"name":"Lijuan Qi","email":"","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Qi","suffix":""},{"id":610961400,"identity":"e064ce67-d5f6-4186-b41c-99c209bcbde1","order_by":3,"name":"Jiefei Peng","email":"","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Jiefei","middleName":"","lastName":"Peng","suffix":""},{"id":610961401,"identity":"5b44c3e3-8c57-4183-a312-692b32403095","order_by":4,"name":"Qiang Feng","email":"","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Feng","suffix":""},{"id":610961402,"identity":"0dd511ec-38f0-439e-9a4e-aaee1a3ffa46","order_by":5,"name":"Zhijun Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACCTDJJsfAkACiidPC2ABUakyyFobEBqK1yEf3mD/88Ycvve94jgHDh7LDDPyzG/BrMbxzxrBBgoctd+aZNwaMM84dZpC4c4CAlhm5GxsMJNhyN9zIMWDmbTvMYCCRQISWBAO2dAOQlr/EaJGXAGo5kMCWANbCSIwWA4n8jzMbDrAZzjzzrOBgz7l0HokbhGyZkZbw8cefY/J8x5M3PvhRZi3HP4OQLQfA1DEGhgNgxMCDXz3IlgYwVQNRPwpGwSgYBaMAGwAAw8pHhtCqgusAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Taian City Central Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-09 02:24:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9067483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9067483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566781,"identity":"e8896d78-44b3-4d5f-b651-15673cea859f","added_by":"auto","created_at":"2026-03-27 12:57:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3293368,"visible":true,"origin":"","legend":"\u003cp\u003eTCGA database analysis showing expression levels and statistical significance of 5′tRF-GlyCCC and IL17A in lung cancer (A, E), along with their relationship to survival (B, C, F, G) and tumor stage (D, H). LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/2b09cfa401dd6792256ee796.jpg"},{"id":105456861,"identity":"ecd199a1-613b-4fc2-a8ed-0e8adc0436cf","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3630648,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between 5′tRF-GlyCCC, IL17A, and different immune cells. (A, C) Heatmaps showing the correlation of 5′tRF-GlyCCC and IL17A with various immune cell populations. (B, D) Correlations between 5′tRF-GlyCCC, IL17A, and different Immune Scores. LUAD, lungadenocarcinoma; LUSC, lung squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/1226f683befb15f2bb557e90.jpg"},{"id":105566591,"identity":"353ab9e0-0053-4cff-81a0-0c32875be5bc","added_by":"auto","created_at":"2026-03-27 12:56:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4411166,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization and identification of exosomes. (A) Transmission electron microscopy (TEM) image of exosomes. (B) Measurement of exosomal size distribution using a qNano particle size analyzer. (C) Detection of exosomal surface markers (TSG101, CD9, and CD81).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/c983f182abc743608455abbc.jpg"},{"id":105456859,"identity":"b2ad010e-bad1-4dc8-9689-b32170618907","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1320539,"visible":true,"origin":"","legend":"\u003cp\u003eSerum 5′tRF-GlyCCC, i-tRF-AlaCGC, neutrophil-to-lymphocyte ratio (NLR), and IL-17 as potential biological diagnostic markers for NSCLC and early-stage NSCLC patients. (A, B) Expression levels of serum exosomal 5′tRF-GlyCCC and i-tRF-AlaCGC in NSCLC patients and healthy donors. (C, D) Serum N/L and IL-17 expression levels in NSCLC patients and healthy donors (HDs). (E, F) Exosomal 5′tRF-GlyCCC and i-tRF-AlaCGC in early-stage NSCLC patients and HDs. (G, H) Serum N/L and IL-17 expression levels in early-stage NSCLC patients and HDs. (**** P \u0026lt; 0.0001, P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/1744d0f82d30e05759c5e091.jpg"},{"id":105456865,"identity":"afb80641-0c6a-4f3a-b21b-9597f0fa2424","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1159358,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between 5′tRF-GlyCCC and i-tRF-AlaCGC expression and tumor grading and metastasis. Expression levels of 5′tRF-GlyCCC across T (A), N (B), and overall stage (C). Serum exosomal i-tRF-AlaCGC expression in T (D) and N (E) stages (P \u0026lt; 0.05; ns, not significant).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/0c4e6a21b504caa37e988c00.jpg"},{"id":105456864,"identity":"fe157fac-4b7b-4e55-b921-dc07d094cfa8","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1209100,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic efficiency of serum 5′tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17 expression levels in NSCLC patients. ROC curves depicting AUC values for (A) 5′tRF-GlyCCC, (B) i-tRF-AlaCGC, (C) N/L, and (D) IL-17. Combined diagnostic performance of (E) 5′tRF-GlyCCC and i-tRF-AlaCGC, (F) both tRF RNAs with IL-17, (G) both tRF RNAs with N/L, and (H) all four predictive markers.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/6e8bb05e4969460cee4168b8.jpg"},{"id":105566490,"identity":"c90424d9-9ab6-4eed-bc6a-5ca9365534a8","added_by":"auto","created_at":"2026-03-27 12:56:31","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1232909,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic efficiency of serum 5′tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17 expression levels in early-stage NSCLC patients. ROC curves showing AUCs for (A) 5′tRF-GlyCCC, (B) i-tRF-AlaCGC, (C) N/L, and (D) IL-17 in early-stage NSCLC patients relative to healthy donors. Combined diagnostic performance of (E) 5′tRF-GlyCCC and i-tRF-AlaCGC, (F) both tRF RNAs with IL-17, (G) both tRF RNAs with N/L, and (H) all four predictive factors.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/731ac6f60fb724631ea46d5f.jpg"},{"id":105456862,"identity":"b140cd0e-0324-4b41-9f88-e4eb7867ed81","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3534742,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical staining confirming IL-17A (A) and CCL20 (B) expression in NSCLC tissue. Magnification: 400×. The red box indicates positive lymphocyte staining.\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/2dd2329e59414656f7866e86.jpg"},{"id":105570200,"identity":"28e30645-9923-47c1-8548-5e25cbead08c","added_by":"auto","created_at":"2026-03-27 13:15:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20884155,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/eb8fddbf-2d43-4693-8fb8-dc09b77d7c6a.pdf"},{"id":105456860,"identity":"ad672bf4-b148-44da-beb6-901dec09262f","added_by":"auto","created_at":"2026-03-26 09:16:14","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1104873,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Survival analysis based on four predictive factors in NSCLC patients: 5′tRF-GlyCCC (A), i-tRF-AlaCGC (B), Neutrophil-to-Lymphocyte ratio (NLR) (C), and IL-17 (D).\u003c/p\u003e","description":"","filename":"Supply1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9067483/v1/74284a997538456474b72159.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conventional serum inflammatory mediators IL-17 combined with exosomal 5'tRF-GlyCCC and i- tRF-AlaCGC are evaluated as a novel biomarker for the early diagnosis of non-small cell lung cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer remains a critical global health challenge, with persistently high incidence and mortality rates. Notably, non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases in Asia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. NSCLC is classified into three primary histological subtypes: adenocarcinoma (originating from mucinous gland cells), squamous cell carcinoma (arising from the squamous epithelium of the airways), and large cell carcinoma. Although rare, adenosquamous carcinoma (a mixture of squamous cell carcinoma and adenocarcinoma) and sarcomatoid carcinoma (a mixture of carcinoma and sarcoma) also exist. The incidence of non-small cell lung cancer differs significantly between men and women. The harmful habit of smoking among men exacerbates lung cancer rates, particularly as smoking increasingly affects younger age groups. Reports indicate that since 2020, smokers account for 20% of the global population, with 15.4% of smokers falling within the 15\u0026ndash;24 age bracket [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Early diagnosis and timely intervention are pivotal to reducing NSCLC mortality. In clinical practice, accurate identification of predictive biomarkers for early diagnosis can provide patients with more treatment opportunities and improved outcomes.\u003c/p\u003e \u003cp\u003etRNA-derived small RNAs (tDRs) are fragments generated from precursor or mature tRNAs, typically ranging from 14 to 50 nucleotides in length. They represent a novel class of small non-coding RNAs (ncRNAs) produced through enzymatic cleavage of tRNAs. Based on their biogenesis sites, tDRs are broadly classified into two categories: tRNA halves (tiRNAs), longer fragments (30\u0026ndash;50 nt) generated by cleavage in the anticodon loop under stress conditions, and tRNA-derived small RNA fragments (tRFs), shorter fragments (14\u0026ndash;30 nt) produced by processing at other tRNA regions (e.g., 5'-tRFs, 3'tRFs, and i-tRFs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The biogenesis of tRFs is not a random process resulting from tRNA degradation but is instead controlled by a highly conserved and precise site-specific cleavage mechanism [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Depending on the cleavage position, tRFs can be classified into 5\u0026prime;-tRFs, 3\u0026prime;-tRFs, and intermediate tRFs (i-tRFs). 5\u0026prime;-tRFs are derived from the 5\u0026prime; end of mature tRNAs through cleavage at the D-loop. 3\u0026prime;-tRFs are formed by specific cleavage at the 3\u0026prime; end of mature tRNAs in the T-loop region, whereas i-tRFs are generated from cleavage of the 3\u0026prime;-trailer sequence of precursor tRNAs. As a representative class of non-coding RNAs, tRFs exhibit diverse biological functions similar to microRNAs, particularly in transcriptional and post-transcriptional regulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They play important roles in tumorigenesis, stem cell biology, and stress responses [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExosomes are membrane-bound vesicles formed through the inward budding of cellular membranes, serving as crucial mediators of intercellular communication. These nanoscale particles are abundantly present in blood and various other bodily fluids, carrying biologically important information from their host cells. In cancer research, exosomes isolated from patient serum have demonstrated significant investigative value [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].As essential delivery vehicles, exosomes are extensively studied for their applications in drug transport and cancer therapeutics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In novel clinical diagnostics, the molecular cargo contained within exosomes enables real-time monitoring of tumor development and accurate assessment of disease progression. This property enables clinicians to access reliable biomarkers for timely cancer diagnosis and informed treatment decision-making.\u003c/p\u003e \u003cp\u003eIL-17 is an effector cytokine produced by Th17 cells and serves as a key inflammatory mediator in various diseases, including allergic reactions, autoimmune disorders, asthma, chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It plays a significant regulatory and promotive role in pulmonary pathologies by mediating inflammatory responses. Evidence suggests that IL-17A, both within the tumor microenvironment and circulating in the blood, correlates with disease progression in lung cancer patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies have linked IL-17 to multiple pathological conditions such as chronic inflammation, cancer, and autoimmune diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], In lung cancer metastasis, IL-17A and γδ T cells play crucial roles, and anti-IL-17 antibody therapy has shown potential in suppressing lung cancer cell growth [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings underscore the therapeutic significance of IL-17 in lung cancer, although its diagnostic value for early detection warrants further evaluation.\u003c/p\u003e \u003cp\u003eThe tumorigenic neutrophil-to-lymphocyte ratio (N/L, NLR), commonly used to assess infection status in immunocompromised patients, has been associated with tumor-promoting granulocytes in breast cancer. This raises an important research question: Could NLR serve as an early predictive marker for NSCLC?\u003c/p\u003e \u003cp\u003eThe diagnostic efficiency of combining serum exosomal tRF RNAs with IL-17 for NSCLC remains unknown. Our study found that 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC were downregulated in the serum of NSCLC patients. The primary focus of this research is to evaluate the diagnostic value of these two tRFs, particularly for the early detection of NSCLC, and to assess the combined diagnostic efficiency when integrated with NLR and IL-17 indicators.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and data statistics\u003c/h2\u003e \u003cp\u003eSamples from 241 NSCLC patients and 201 healthy adult controls were collected between March and August 2021 at Tai'an City Central Hospital. All NSCLC blood samples were obtained before treatment, while the healthy control samples were collected from individuals undergoing routine physical examinations. The expression levels of different tRF RNAs in serum, along with detailed clinical information of lung cancer patients, including gender, age, cancer type, lifestyle factors, and tumor metastasis, were compared with those of healthy controls. Approximately 3 mL of heparin-anticoagulated blood was collected in tubes containing separator gel and centrifuged at 4\u0026deg;C and 1000 \u0026times; g for 10 min to obtain serum samples, which were then stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent experiments.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Preparation\u003c/h3\u003e\n\u003cp\u003eData for lung adenocarcinoma and lung squamous cell carcinoma were downloaded from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze the relationship between prognostic factors and patient outcomes. The infiltration levels of various immune cells in tumor patients, including B cells, T cells, and dendritic cells (DCs), were reassessed based on gene expression profiles. Using R software (version 3.6.4), expression differences between normal and tumor samples within each tumor type were calculated. The unpaired Wilcoxon rank-sum test and paired Wilcoxon signed-rank test were applied to evaluate the statistical significance of these differences.\u003c/p\u003e\n\u003ch3\u003eExtraction of exosomes and performance testing\u003c/h3\u003e\n\u003cp\u003eSerum collected from NSCLC patients and healthy controls was thawed on ice and centrifuged at 10,000\u0026times;g, 4\u0026deg;C for 30 min to retain the supernatant and remove the precipitate. The supernatant was then centrifuged at 100,000\u0026times;g, 4\u0026deg;C for 2 h to remove residual debris, followed by another centrifugation at 120,000\u0026times;g, 4\u0026deg;C for 2 h to isolate exosomes (Type 50.4 Ti Rotor; Beckman Coulter), which is highly efficient for exosome extraction. The extraction method was performed according to previously published protocols [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe size and distribution of exosomes were determined using a particle size analyzer (qNano, New Zealand). Fifteen microliters of PBS-washed exosome samples were added to a copper grid. After natural drying, the physical morphology of the exosomes was observed with a FEI Tecnai T20e transmission electron microscope (FEI Company, USA).\u003c/p\u003e \u003cp\u003eWestern blot analysis was performed to identify exosomal outer membrane proteins using a PVDF membrane (Millipore, Billerica, MA, USA). According to the literature [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], three proteins highly expressed on the surface of exosomes were selected for detection: TSG101 (ProteinTech, USA, 1:800), CD81 (ProteinTech, USA, 1:1000), and CD9 (ProteinTech, USA, 1:800). Rabbit anti-exosome antibodies (Abmart, 1:5000) were used, and the membranes were incubated at 37\u0026deg;C for 1 h to visualize the protein bands.\u003c/p\u003e\n\u003ch3\u003eRNA extraction and reverse transcription\u003c/h3\u003e\n\u003cp\u003eThe extracted exosomes were resuspended in PBS, and 1 mL of TRIzol was added to lyse the exosomes for RNA extraction. The extraction method followed the conventional lysis protocol, and the extracted RNA was subsequently reverse transcribed into cDNA for the next qPCR procedure (TaKaRa Bio, Nojihigashi, Kusatsu, Japan). The resulting cDNA was stable and suitable for storage, typically kept at \u0026minus;\u0026thinsp;20\u0026deg;C. Quantitative PCR (qPCR) assays were then performed according to the following steps.\u003c/p\u003e\n\u003ch3\u003eqPCR\u003c/h3\u003e\n\u003cp\u003eqPCR was performed to detect the expression of tRF RNA in the serum of healthy controls and patients with NSCLC. U6 was used as the internal reference (F: 5'-CTCGCTTCGGCAGCACA-3', R: 5'-AACGCTTCACGAATTTGCGT-3\u0026prime;). Statistical analysis was conducted based on the measured ΔCT values, and all experiments were independently repeated three times (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of IL17 and NLR\u003c/h2\u003e \u003cp\u003eSera collected from healthy controls and NSCLC patients were centrifuged at 3000 rpm for 3 min to remove the supernatant. The expression of IL-17 (Qingdao Risikeer Biotechnology Co., Ltd.) in the sera of procoagulant samples was determined using flow cytometry (NovoCyte D20, ACEA Biosciences). The NLR was measured in the sera of procoagulant samples. The test samples were anticoagulated with EDTA, and the numbers of granulocytes and lymphocytes were determined separately using a hematology analyzer (XN-10 [B4], Mindray). The NLR was then calculated based on these values.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry\u003c/h3\u003e\n\u003cp\u003eTissue specimens from patients with NSCLC were sectioned into paraffin-embedded slices with a thickness of 5 \u0026micro;m. These sections underwent a series of procedures, including dewaxing and hydration, antigen retrieval, incubation with primary antibodies against IL-17A (Bioworld, China, 1:150) and CCL20 (Abmart, China, 1:200), incubation with secondary antibodies, chromogenic development, hematoxylin staining, and sealing with coverslips. The results were obtained by scanning the sections under a microscope. Both cancerous and adjacent paracancerous tissues were obtained from the same patient specimens.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe first analyzed the qPCR data to determine whether they conformed to a normal distribution. The t-test was used for normally distributed data, and the Mann\u0026ndash;Whitney unpaired test was applied for non-normally distributed data. Differences between the analyzed measurements were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All data were statistically analyzed using GraphPad Prism 9.5 (GraphPad Software, Inc., CA, USA). The area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of candidate predictors using SPSS 22.0 (Ehningen, Germany).\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003eAnalysis of Predictive Factor Expression in the TCGA Database\u003c/h2\u003e\n \u003cp\u003eMultidimensional chart analysis revealed the expression characteristics and clinical correlations of the target genes 5\u0026prime;tRF-GlyCCC (ENSG00000253603) and IL17A in lung cancer (LUAD, LUSC). 5\u0026prime;tRF-GlyCCC exhibited statistically significant downregulation in NSCLC (Figs. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C). Analysis indicated that low 5\u0026prime;tRF-GlyCCC expression correlated with poor prognosis, showing statistical significance in LUAD. The relationship between 5\u0026prime;tRF-GlyCCC expression and tumor stage was further analyzed (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eAnalysis of the TCGA database revealed downregulation of IL17A in both LUAD and LUSC (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). High IL17A expression was associated with poor prognosis in early-stage patients (Figs. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G). Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH illustrates the relationship between IL-17A expression and tumor stage, showing significantly higher levels of IL-17A in patients with advanced-stage tumors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHeatmap of Correlation Between Expression Levels of 5\u0026apos;tRF-GlyCCC and IL17A with Immune Cells in Non-Small Cell Lung Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis study employed visual heatmap analysis to reveal the association patterns between 5\u0026prime;tRF-GlyCCC and IL17A expression and multiple immune cells within the tumor immune microenvironment. The purple spectrum indicates the statistical significance of correlations (darker purple represents lower p-values), while the red\u0026ndash;blue spectrum denotes the direction and strength of correlation coefficients (red indicates a positive correlation and blue indicates a negative correlation) (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003eSpecifically, statistically significant correlations between 5\u0026prime;tRF-GlyCCC and plasma cells, CD4⁺ memory T cells, and T-follicular helper cells are indicated by asterisks (*). Different levels of correlation were observed across varying degrees of cellular infiltration. Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB evaluates correlations with the Immune Score. In LUSC patients, 5\u0026prime;tRF-GlyCCC expression shows a statistically significant negative correlation with immune cell types.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC demonstrates that IL17A exhibits statistically significant correlations with B cells, CD8⁺ T cells, Treg cells, and other immune cells, also indicated by asterisks (*). It further reveals varying degrees of correlation with immune cell infiltration levels. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD displays a significant positive correlation between IL17A expression and the Immune Score, with statistically significant differences.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of Serum Exosomes\u003c/h2\u003e\n \u003cp\u003eExosome samples extracted via ultracentrifugation underwent identification and analysis. Transmission electron microscopy (Figs. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B) revealed spherical exosomes with diameters predominantly clustered around 100 nm, exhibiting a normal size distribution. Further characterization of the exosomal vesicles was performed via Western blot analysis, which confirmed the presence of characteristic exosomal surface proteins. These proteins displayed expression patterns distinct from those observed on the cellular membrane, with exosomes showing high expression of TSG101, CD9, and CD81 (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These results confirm the successful isolation and identification of exosomes.\u003c/p\u003e\n \u003cp\u003eHigh-throughput gene chip sequencing identified 24 tRF RNAs with characteristic expression profiles (Table 1), comprising 12 significantly upregulated and 12 downregulated transcripts. Sequencing samples included three healthy control groups, three non-metastatic NSCLC groups, and three metastatic NSCLC groups. Subsequent validation experiments were conducted using qPCR analysis.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExpression of 5\u0026rsquo;tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL17 in Serum from Non-Small Cell Lung Cancer Patients and Early-Stage Patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSerum exosome-derived tRF RNA was subjected to reverse transcription and real-time qPCR. The results revealed that both 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC expression levels were significantly downregulated in serum exosomes from 242 NSCLC patients compared with 201 healthy controls (Figs. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). The NLR was significantly elevated in the serum of NSCLC patients, indicating increased granulocyte activity (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The inflammatory cytokine IL-17 was also markedly overexpressed in serum samples (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), showing a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003cp\u003eIn serum exosomes from 93 early-stage NSCLC patients, 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC expression levels were also significantly downregulated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Figs. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). Similarly, the tumorigenic N/L ratio was substantially higher in NSCLC serum samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005; Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). IL-17 expression was also significantly increased in serum (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eRelationship Between Predictors and Clinical Pathological Grading\u003c/h2\u003e\n \u003cp\u003eClinical data from patients were collected for pathological analysis. 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC expression levels were statistically analyzed in relation to individual patient characteristics. The data indicated that 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC showed no statistically significant differences across patient gender, age, pathological type, lifestyle habits, or tumor metastasis status (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSurvival duration in NSCLC patients was correlated with tumor grading, emphasizing the importance of early diagnosis as a research priority. Our screening revealed no statistically significant association between 5\u0026prime;tRF-GlyCCC or i-tRF-AlaCGC expression and primary tumor T stage (Figs. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, D). Similarly, no significant correlation was observed between the expression of these genes and lymph node metastasis (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Figs. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, E).\u003c/p\u003e\n \u003cp\u003eHowever, 5\u0026prime;tRF-GlyCCC exhibited a statistically significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between stage I and stage III tumors but showed no significance between other stages (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). i-tRF-AlaCGC showed no statistical significance across clinical stages in NSCLC patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eEfficacy of Predictors in Diagnosing NSCLC\u003c/h2\u003e\n \u003cp\u003eThe diagnostic accuracy of 5\u0026prime;tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17 as diagnostic markers for NSCLC was evaluated using receiver operating characteristic (ROC) curve analysis and quantified by the area under the curve (AUC). The diagnostic efficiency of 5\u0026prime;tRF-GlyCCC was 0.669 (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), while that of i-tRF-AlaCGC was 0.673 (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The tumorigenic N/L ratio also demonstrated high diagnostic efficiency with an AUC of 0.745, and IL-17 achieved the highest single-marker efficiency at 0.902 (Figs. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D).\u003c/p\u003e\n \u003cp\u003eCombined analysis of 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC improved diagnostic efficiency to 0.687 (95% confidence interval [CI]: 0.636\u0026ndash;0.737; specificity 0.442; accuracy 0.884) (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). When both tRF RNAs were combined with IL-17, diagnostic performance increased significantly, yielding an AUC of 0.905, while combining the two tRF RNAs with tumorigenic N/L also produced a high AUC of 0.806 (Figs. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). The combination of all four predictive markers, 5\u0026prime;tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17, demonstrated excellent diagnostic efficiency for NSCLC, achieving an AUC of 0.926 (95% CI: 0.901\u0026ndash;0.951; specificity 0.899; sensitivity 0.822) (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e\n \u003cp\u003eSurvival analysis of all NSCLC cases revealed that low expression levels of 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC were associated with poorer prognosis, although the differences were not statistically significant (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). Conversely, high expression levels of IL-17 and N/L were significantly correlated with reduced survival rates (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eFour predictors for early diagnosis of non-small cell lung cancer\u003c/h2\u003e\n \u003cp\u003eEarly diagnosis of NSCLC remains a primary focus of research. Using SPSS statistical analysis, the early diagnostic efficiencies (AUC values) of 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC were 0.640 and 0.649, respectively (Figs. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). The early diagnostic efficiency of N/L was also relatively high, with an AUC of 0.578, whereas IL-17 exhibited a notably strong diagnostic performance, achieving an AUC of 0.901 (Figs. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D).When combined, 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC improved early diagnostic efficiency to 0.654. The combination of both tRF RNAs with IL-17 further enhanced early diagnostic value, reaching an AUC of 0.911. Additionally, the combination of the two tRF RNAs with N/L yielded a high AUC of 0.687 (Figs. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F).\u003c/p\u003e\n \u003cp\u003eThe combined use of all four predictors, 5\u0026prime;tRF-GlyCCC, i-tRF-AlaCGC, N/L, and IL-17, demonstrated the highest early diagnostic efficiency for NSCLC, achieving an AUC of 0.919 (95% confidence interval [CI]: 0.626\u0026ndash;0.734; specificity 0.915; accuracy 0.806) (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003eG).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eImmunohistochemistry Aids Diagnosis of NSCLC\u003c/h2\u003e\n \u003cp\u003eImmunohistochemical staining provided direct histological validation, demonstrating that IL-17A is significantly overexpressed in NSCLC tissues. This overexpression was particularly prominent in lymphocyte-rich regions (red-boxed areas), where cytoplasmic IL-17A staining was markedly elevated. Enhanced cytoplasmic IL-17A expression was also evident within tumor tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). CCL20, a pro-inflammatory cytokine abundantly secreted by granulocytes, was likewise highly expressed in NSCLC tissue specimens, displaying pronounced cytoplasmic localization and strong staining intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence and mortality rates of lung cancer remain high, and its etiology is complex [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].In particular, the lack of personal health awareness among patients leads to low diagnosis and cure rates [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The non-invasive diagnostic rate for NSCLC remains low, especially for early-stage detection, which is a major factor leading to poor survival outcomes in NSCLC patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, we combined tRF RNA markers with the inflammatory cytokine IL-17 to improve the diagnostic accuracy for NSCLC, while also exploring the elevated expression of inflammatory cells in affected patients. Previous studies have demonstrated that IL-17 expression increases with tumor progression, whereas the number of Th17 cell infiltrates decreases in patients with advanced-stage disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Using the TCGA database, we analyzed the expression of predictive factors 5\u0026prime;tRF-GlyCCC, i-tRF-AlaCGC, and IL-17A in serum samples from NSCLC patients. However, data related to i-tRF-AlaCGC were not available in the database, so only 5\u0026prime;tRF-GlyCCC and IL-17A were analyzed. Our findings showed that 5\u0026prime;tRF-GlyCCC expression was not correlated with tumor stage.\u003c/p\u003e \u003cp\u003ePrevious research has shown that IL-17A and its downstream signaling molecules, including ERK1/2, NF-κB, MMPs, and VEGF, represent potential molecular targets for cancer prevention and therapy, particularly in breast cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Further investigations by Beibei Xu, Esra A. Akbay, et al. demonstrated that elevated IL-17A expression in lung cancer promotes tumor proliferation through IL-6 and tumor-associated neutrophils. Their analyses also revealed that IL-17A mediates resistance to programmed cell death protein 1 (PD-1) blockade therapy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our study, oncogenic neutrophils and granulocytes were significantly elevated in the serum of NSCLC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and their increased expression was strongly associated with poor prognosis. Therefore, we incorporated the NLR, a routinely available hematological index, into our NSCLC diagnostic model.\u003c/p\u003e \u003cp\u003eC-C motif chemokine ligand 20 (CCL20) has been linked to poor prognosis in breast cancer patients. The CCL20\u0026ndash;CCR6 axis directly facilitates tumor progression by promoting cancer cell proliferation and migration, and indirectly remodels the tumor microenvironment through the regulation of immune cell [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Tumor-derived CCL20, activated by Fusobacterium nucleatum (Fn), enhances colorectal cancer metastasis and contributes to tumor microenvironment reprogramming [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, the overexpression of circSMARCC1 promotes CD163 expression in macrophages through the CCL20\u0026ndash;CCR6 pathway, leading to tumor-associated macrophage infiltration and M2 polarization, which drives prostate cancer progression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Research by Dan Wang et al. further suggests that serum CCL20 combined with IL-17A serves as an early diagnostic and prognostic biomarker for human colorectal cancer. Based on these findings, our study highlights the combined diagnostic value of IL-17A, tRF RNA, and the N/L ratio, three biologically and clinically relevant markers, as promising predictive indicators for the early detection of NSCLC.\u003c/p\u003e \u003cp\u003eSerum exosomes contain crucial genetic information derived from patients, and analyzing their molecular components is essential for improving cancer diagnostic accuracy. Exosomes carry various biomolecules that facilitate intercellular communication and play vital roles in tumor initiation, progression, and metastasis, key areas of focus in this study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As one of the representatives of non-coding RNAs, tRF RNA participated in gene regulation and modification in vivo, particularly in post-transcriptional regulation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In cancer, tRF RNAs modulate intracellular signaling, thereby influencing the proliferation of tumor cells. For instance, tRF3008A suppresses colorectal cancer development by inhibiting endogenous FOXK1 expression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, 3\u0026prime;tRF-AlaAGC promotes malignant activity in breast cancer cells and induces M2 macrophage polarization both in vitro and in vivo, with elevated M2 macrophage expression correlating with lymph node metastasis and deeper tumor invasion [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, our prior research identified a novel anti-cancer mechanism of 25(OH)D, demonstrating the role of tRF-1-Ser in breast cancer progression and proposing it as a potential therapeutic target [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC are tRNA-derived fragments, and we report for the first time their significantly reduced expression in the serum of patients with NSCLC. Validation using gene chip sequencing confirmed the diagnostic relevance of these findings. The combined diagnostic AUC of these two markers for NSCLC was 0.687 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), while for early-stage NSCLC it was 0.654. Although their individual diagnostic efficiencies were moderate, combining them with inflammatory markers markedly enhanced diagnostic performance. In particular, tumor-associated neutrophil infiltration significantly increased diagnostic accuracy for NSCLC. Future studies will focus on elucidating the molecular signaling mechanisms that drive NSCLC metastasis, aiming to identify new therapeutic targets.\u003c/p\u003e \u003cp\u003eImmunohistochemical staining revealed strong overexpression of CCL20 in NSCLC tissues. As a marker of tumor-promoting neutrophil infiltration, CCL20 serves as a key indicator for predicting and validating the progression of NSCLC. Targeting the IL-17 cytokine or its associated pathways may represent a promising therapeutic strategy. However, IL-17 alone is insufficient to fully characterize cancer progression. Th17 cells are the primary source of IL-17A (IL-17), while γδ T cells are important initiators of inflammation. Accordingly, our immunohistochemical validation focused on IL-17A expression [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo achieve a more comprehensive evaluation, we incorporated non-coding tRF RNAs into the NSCLC diagnostic assessment. The diagnostic performance was substantially enhanced when IL-17 was combined with the NLR, resulting in an AUC of 0.926. In contrast, the combination of tRF RNA with NLR alone yielded an AUC of 0.806, indicating notable improvement. For the first time, we identified and applied 5\u0026rsquo;tRF-GlyCCC, i-tRF-AlaCGC, IL-17, and NLR as integrated biomarkers for the early diagnosis of NSCLC, demonstrating their effectiveness as a cost-efficient and highly accurate diagnostic approach.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTheir diagnostic efficacy is markedly enhanced when combined with IL-17 and the neutrophil-to-lymphocyte (N/L) ratio. 5\u0026prime;tRF-GlyCCC and i-tRF-AlaCGC represent newly identified biomarkers with strong potential for the early diagnosis of NSCLC. Histological analysis further validates the high diagnostic value of IL-17A and CCL20. Together, these low-cost and easily accessible biomarkers hold promise as effective tools for early clinical detection and risk assessment in NSCLC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by The\u0026nbsp;Shandong Provincial Medical and Health Science and Technology Projects (202511000079)\u0026nbsp;and\u0026nbsp;Tai\u0026apos;an City Science and Technology Innovation Development Project (No.2024NS293,\u0026nbsp;No.2023NS419).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in the design, interpretation of the studies and analysis of the data and review of the manuscript; LD and ZJZ designed the experiments; YYS and LD performed the experiments and analyzed the data; LJQ and YYS reviewed and analyzed clinical medical records; JFP wrote the original manuscript, which was reviewed by QF and ZJZ. The final manuscript has the approval of all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Duan, Yanyan Sang, Lijuan Qi, Jiefei Peng, Qiang Feng and Zhijun Zhang declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Ethics Committee of The Affiliated Taian City Central Hospital of Qingdao University, with the ethics approval numbe: 20230510. All subjects gave their informed consent in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients consented to use their tissue, clinical, and pathological information for the experimental research, and all signed an informed consent form. The specimens we used were discarded samples from the clinical laboratory, so no clinical experiments were involved. Clinical trial is not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur raw data, and manuscript did not contain any individual details, images, or videos. The authors used to number the cases to maintain confidentiality of patient data.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXi Z, Dai R, Ze Y, et al. 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Nat Rev Immunol. 2017;17:535\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nri.2017.50\u003c/span\u003e\u003cspan address=\"10.1038/nri.2017.50\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"668\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"7\" style=\"width: 593px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: tRFRNA expression profiling (12 up-regulated and 12down-regulated)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003etRFRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFold change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 123px;\"\u003e\n \u003cp\u003etRFRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFold change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-LySCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.2820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0352*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tiRNA-ArgCCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0252*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-ProAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.1272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0096**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tRF-ArgTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0091**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tRF-ArgACG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.5124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0406*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tRF-TyrGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0020**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003ei-tRF-LeuCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.6801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0298*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-GlyCCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0127*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003ei-tRF-SerGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.4099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0068**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tiRNA-SerCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.3619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0048**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-ASnATT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.4070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0192*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tiRNA-MetCAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0075**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-IleAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.3299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0062**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003ei-tRF-TrpCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0007***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-SerAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.4687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0018**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tiRNA-HisGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0080**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-ThrTGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.2617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0040**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tRF-ValTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0148*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-CysGCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.1212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0289*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tRF-AlaAGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0104*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-AlaTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.8001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0435*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u0026apos;tiRNA-LeuAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0081**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5\u0026apos;tRF-ValAAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.5470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 83px;\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0064**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003ei-tRF-AlaCGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 75px;\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0281*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"8\" valign=\"bottom\" style=\"width: 668px;\"\u003e\n \u003cp\u003e*P\u0026lt;0.05;\u0026nbsp;**P\u0026lt;0.01;\u0026nbsp;***P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"8\" style=\"width: 53.6702%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Characteristics of NSCLC patients for differentially expressed exosomes 5\u0026rsquo;tRF-1-GlyCCC and i-tRF-AlaCGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" style=\"width: 11.5512%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNo. cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 22.0878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u0026rsquo;tRF-1-GlyCCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNo. cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 21.7756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ei-tRF-AlaCGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMedian with interquartile range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian with interquartile range\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003e\u0026le;62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.9713（-2.652~0.2039）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.0676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.315\u0026nbsp;（-4.349~-0.7397）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.3449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003e\u0026gt;62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.4646（-2.124~0.7534）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.255（-3.324~-1.081）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.7429（-2.327~0.6867）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.3962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.329（-3.772~-0.9620）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.5520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.8707（-2.324~0.4413）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.160（-3.670~-0.9668）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.5515（-2.308~0.8004）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.2254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.321（-3.756~-0.8343）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.7531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.5415%;\"\u003e\n \u003cp\u003ehistory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003enon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.8911（-2.361~0.2908）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.240（-3.67\u003c/p\u003e\n \u003cp\u003e8~-1.064）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eDrinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.5270（-2.281~0.7042）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.2556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.383（-3.652~-0.9359）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.9039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.5415%;\"\u003e\n \u003cp\u003ehistory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003enon-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.9014（-2.439~-0.5022）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.160（-3.699~-1.018）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.9364（-2.476~0.2069）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.1812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.276（-3.796~-1.016）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.7711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.5773（-2.170~0.7626）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.293（-3.481~-0.7899）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5.5415%;\"\u003e\n \u003cp\u003eDistant \u0026nbsp;metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.8377（-2.386~0.3985）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e0.2463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.271（-3.661~-0.7750）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e0.9934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 6.0098%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 3.5902%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.639%;\"\u003e\n \u003cp\u003e-0.3370（-2.071~0.8318）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e-2.048（-3.477~-0.9911）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"4\" style=\"width: 32.7805%;\"\u003e\n \u003cp\u003eNSCLC, non-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.4488%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 2.9659%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 17.561%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 4.2146%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"IL17, N/L, 5'tRF-GlyCCC, i-tRF-AlaCGC, Exosomes, AUC","lastPublishedDoi":"10.21203/rs.3.rs-9067483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9067483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIL-17, an inflammatory cytokine secreted by Th17 cells, plays a pivotal role in tumor immunity and inflammation. Among its subtypes, IL-17A functions as the principal effector. Neutrophil-to-lymphocyte ratio (N/L, NLR) serves as a routine clinical indicator for infection screening, and elevated granulocytes in tumor tissues reflect tumorigenic activity. This study investigated the combined diagnostic and prognostic potential of serum exosomal 5'tRF-GlyCCC and i-tRF-AlaCGC in relation to IL-17 and NLR in non-small cell lung cancer (NSCLC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBioinformatic analyses explored the biological functions of 5'tRF-GlyCCC and IL-17A using TCGA data. Serum IL-17 levels were quantified by flow cytometry, and NLR was calculated from routine blood tests. Exosomes were isolated from serum and characterized via transmission electron microscopy, particle size analysis, and Western blotting. Differentially expressed tRFs were identified by microarray and validated by qPCR in 242 NSCLC patients and 201 healthy controls. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTCGA analysis showed significant differential expression of 5'tRF-GlyCCC and IL17A in cancer patients, with low 5'tRF-GlyCCC expression associated with poor prognosis. NSCLC patients exhibited elevated levels of IL-17 and NLR. Both 5'tRF-GlyCCC and i-tRF-AlaCGC were markedly downregulated in NSCLC (AUC: 0.669 and 0.673) and early-stage disease (AUC: 0.640 and 0.649). The combined AUC for diagnosing NSCLC using all four predictors was 0.926, and for early-stage diagnosis it was 0.901. Abnormally expression of these predictors correlated with poor patient prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIL-17 and NLR, combined with serum exosomal 5'tRF-GlyCCC and i-tRF-AlaCGC, show strong diagnostic and prognostic potential for early detection of NSCLC.\u003c/p\u003e","manuscriptTitle":"Conventional serum inflammatory mediators IL-17 combined with exosomal 5'tRF-GlyCCC and i- tRF-AlaCGC are evaluated as a novel biomarker for the early diagnosis of non-small cell lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 09:15:49","doi":"10.21203/rs.3.rs-9067483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T11:09:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T08:30:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T10:04:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37589936803502598105806059892066209825","date":"2026-03-23T15:55:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69001876363933523959386854798908780045","date":"2026-03-23T15:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-23T14:46:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T12:55:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T11:46:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-03-13T07:01:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f3da080-28be-4990-bf37-2f0f9618e17b","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T16:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 09:15:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9067483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9067483","identity":"rs-9067483","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}