Circulating lncRNAs as Early Diagnostic Biomarkers for Colorectal Cancer: A case-control diagnostic accuracy study

Circulating lncRNAs as Early Diagnostic Biomarkers for Colorectal Cancer: A case-control diagnostic accuracy study | 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 Article Circulating lncRNAs as Early Diagnostic Biomarkers for Colorectal Cancer: A case-control diagnostic accuracy study Xiaoyi Bai, Qing Lu, Lihong Ma, Kai Deng, Xiuhe Lv, Jinlin Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169352/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose This study aimed to identify and validate long non-coding RNAs (lncRNAs) as diagnostic biomarkers for Colorectal cancer (CRC) and its precursors, focusing on early-stage risk stratification. Methods We performed bioinformatics analysis of public datasets and in-house RNA-sequencing data to identify dysregulated lncRNAs in the adenoma–carcinoma sequence. Plasma samples were collected from patients with non-advanced adenomas, advanced adenomas, CRC, and healthy controls. Candidate lncRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, and lncRNA combinations were assessed to improve accuracy. Integration with CEA was also explored. Results We identified 37 differentially expressed lncRNAs, of which four—FOXP4-AS1, CRNDE, UCA1, and SNHG17—showed significant dysregulation in plasma. ROC analysis indicated that UCA1 and SNHG17 effectively distinguished advanced adenomas from healthy controls (area under the curve = 0.835 and 0.812, respectively). The three-lncRNA panel FOXP4-AS1 + UCA1 + SNHG17 achieved an area under the curve of 0.906 for CRC detection and 0.861 for advanced adenomas. Combining lncRNAs with CEA further enhanced diagnostic performance, reaching area under the curve values of 0.950 for CRC and 0.893 for advanced adenomas. Conclusion FOXP4-AS1, CRNDE, UCA1, and SNHG17 are promising plasma-based biomarkers for CRC and adenoma detection. When combined into multi-lncRNA panels, they offer a non-invasive, high-performance approach for early CRC screening and risk-based stratification. Further large-scale, multi-center validation is needed to establish their clinical applicability and integration into routine screening. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Gastroenterology Health sciences/Oncology Colorectal Cancer (CRC) Long Non-Coding RNA (lncRNA) Liquid Biopsy Diagnostic Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related mortality worldwide[ 1 ]. Early detection is pivotal for improving prognosis and reducing disease burden, yet CRC often remains asymptomatic until advanced stages[ 2 ]. Although colonoscopy remains the gold standard for early CRC detection, its invasiveness and high cost limit its widespread application in population-based screening programs[ 3 – 6 ]. In recent years, circulating biomarkers obtained through liquid biopsies have emerged as promising non-invasive alternatives for cancer detection[ 7 ]. In particular, long non-coding RNAs (lncRNAs)—RNA transcripts longer than 200 nucleotides that are not translated into proteins—are increasingly recognized for their regulatory roles in cancer biology[ 8 ]. Aberrant expression of lncRNAs has been implicated in numerous cellular processes, including proliferation, apoptosis, metabolism, and differentiation[ 8 – 10 ]. Importantly, circulating lncRNAs, detectable in body fluids such as plasma, have thus gained significant attention as novel diagnostic and prognostic markers in various malignancies, including CRC[ 11 – 14 ]. While most previous studies on CRC blood-based biomarkers have focused primarily on distinguishing CRC patients from healthy individuals[ 15 , 16 ], there remains a critical unmet need for early-stage risk stratification—particularly the identification of high-risk precancerous lesions. The majority of sporadic CRCs arise through the conventional adenoma–carcinoma sequence[ 17 ], while approximately 25% follow the serrated neoplasia pathway[ 18 ]. Detecting precancerous lesions at different points along this progression, especially distinguishing advanced adenomas from non-advanced adenomas, is crucial for effective early intervention. This study extends previous work by incorporating plasma samples from both non-advanced and advanced adenoma patients, in addition to CRC and healthy controls. By integrating bioinformatics screening and experimental validation, we aim to identify circulating lncRNAs that exhibit progressive dysregulation along the adenoma–carcinoma sequence. Our goal is not only to enable non-invasive early detection of CRC, but also to identify patients with adenomas at high risk of malignant transformation, providing a novel strategy for risk-based screening and personalized surveillance. 2 Methods 2.1 Candidate lncRNA Identification To identify lncRNAs associated with the early development of CRC, we performed a systematic screening using both public datasets and in-house sequencing data. A search of the Gene Expression Omnibus (GEO) database was conducted with the keywords 'colorectal cancer OR adenoma' and 'RNA-Seq' to identify datasets profiling the normal-adenoma-carcinoma sequence. Two datasets, GSE164541 and GSE109203, were selected (details in Table S1 ). Additionally, whole-transcriptome sequencing was conducted on 5 pairs of matched CRC and adjacent normal tissues, and the lncRNA data were extracted for analysis. The criteria for identifying significant differential expression were as follows: for the Normal vs. Adenoma comparison in GSE164541, a threshold of |log2FC| ≥ 1.5 and an adjusted P-value < 0.01 was applied. For all Normal vs. Carcinoma comparisons (in GSE164541, GSE102920, and our in-house sequencing data), a threshold of |log2FC| ≥ 1.5 and an adjusted P-value < 0.05 was used. The Sangerbox online platform ( http://www.sangerbox.com/tool ) and “R” was employed for this data processing and differential gene analysis. 2.2 Sample Collection This study collected plasma samples from West China Hospital of Sichuan University from 2021 to 2023. We recruited patients aged 18 to 75 years with a pathological diagnosis of colorectal adenoma or adenocarcinoma. Participants were categorized into the following groups based on colonoscopic and histopathological findings: (1) Non-advanced Adenoma Group (NAA): Patients with adenomas that did not meet the criteria for advanced adenoma. (2) Advanced Adenoma Group (AA): Patients with adenomas exhibiting at least one of the following high-risk features: size ≥ 10 mm, villous component ≥ 25%, or high-grade dysplasia[ 19 ]. (3) CRC Group: Patients with confirmed invasion of malignant cells through the muscularis mucosa. We also recruited a Healthy Control (HC) Group, comprised of individuals who underwent colonoscopy with normal findings, defined as no detection of adenomas, serrated polyps, or colorectal cancer. The exclusive criterion: (1) patients who received any type of treatment; (2) patients with a concurrent or previous diagnosis of any other type of cancer; (3) individuals with a history of acute clinical events (such as myocardial infarction within the past 6 months) or those with severe chronic comorbidities; (4) use of any anticoagulant or antiplatelet agent medications (such as aspirin or warfarin), or a diagnosed coagulation disorder. Written informed consent was obtained from each individual participant, and the experimental protocol was approved by the West China Hospital Institutional Review Board(NO.2021 − 179. Also all experiments were performed in accordance with relevant guidelines and regulations of the Declaration of Helsinki. 2.3 Sample Processing Peripheral blood samples (15 mL per participant) were collected in EDTA-containing tubes and processed using a two-step centrifugation protocol. To prevent cell lysis, the samples were first centrifuged at 1,600 × g for 10 minutes at 4°C. The supernatant was then carefully transferred and subjected to a second centrifugation at 16,000 × g for 10 minutes at 4°C to remove cellular debris and fragments. The resulting cell-free plasma was aliquoted into RNase-free tubes, preserved with Trizol LS reagent, and stored at − 80°C until RNA extraction and further analysis[ 20 , 21 ]. 2.4 RNA Isolation Total RNA was extracted from plasma samples using TRIzol™ LS Reagent (Thermo Fisher Scientific, USA) according to the manufacturer's protocol. RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Scientific, USA). Complementary DNA (cDNA) was synthesized from 1 µg of total RNA using the HiScript III RT SuperMix for qPCR (+ gDNA wiper) (Vazyme, China) on a SimpliAmp™ Thermal Cycler (Thermo Fisher Scientific, USA) to eliminate genomic DNA contamination and ensure high-quality reverse transcription. 2.5 Quantitative Real-Time PCR (qPCR) qPCR was performed using ChamQ SYBR Color qPCR Master Mix (Vazyme, China) on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, USA). The thermal cycling protocol consisted of an initial denaturation at 95°C for 30 s, followed by 45 cycles of 95°C for 5 s and 60°C for 30 s. After evaluating the stability of candidate genes (GAPDH, β-actin, U6, and 5S rRNA), 5S rRNA was selected as the internal reference due to its superior stability in plasma samples. Primer sequences for target lncRNAs, designed to span exon regions, are detailed in Table S2. Assays with non-specific amplification, primer-dimer formation, or Ct values > 35 were excluded. Relative expression levels were calculated using the ΔCt method, with a larger ΔCt value indicating lower relative expression. 2.6 Statistical analysis All statistical analyses were performed using SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 9.5.0 (GraphPad Software, San Diego, CA, USA). Normality was assessed using the Shapiro-Wilk test. Continuous variables were presented as mean ± SD for normally distributed data or as median with interquartile range [M (P25, P75)] for non-normally distributed data. For comparison of lncRNA expression across the four study groups, the Kruskal-Wallis test was employed followed by Dunn's post-hoc analysis for multiple comparisons. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were employed to assess the diagnostic value of the candidate biomarkers. A logistic regression model was developed to combine the identified lncRNAs with the conventional tumor marker CEA into a composite diagnostic panel. Associations between lncRNA expression levels and clinicopathological characteristics were analyzed using the Mann-Whitney U test. P values < 0.05 were considered statistically significant. 3 Results 3.1 Identification of Candidate lncRNAs Volcano plots in Fig. 1 a illustrate the expression patterns of candidate lncRNAs. By integrating bioinformatics analysis of public datasets and in-house sequencing data, we identified 37 lncRNAs (30 up-regulated and 7 down-regulated) that were consistently dysregulated in both adenoma and carcinoma compared to normal tissues (Fig. 1 b). Detailed data are provided in Table 1 . Table 1 LncRNAs expression in three datasets. LncRNAs GSE164541 A vs N GSE164541 T vs N GSE1029203 T vs N In-house RNA-seq T vs N logFC Description P -value logFC Description P -value logFC Description P -value logFC Description P -value MGC32805 4.71 Up 8.92E-05 3.72 Up 6.64E-04 2.23 Up 1.18E-02 2.35 Up 1.13E-05 CCAT1 4.24 Up 4.53E-04 5.38 Up 1.15E-04 3.06 Up 6.51E-03 4.23 Up 1.18E-08 GATA2-AS1 4.19 Up 2.96E-04 2.25 Up 4.78E-03 2.83 Up 1.76E-03 1.59 Up 4.99E-03 BLACAT1 4.10 Up 8.42E-06 4.19 Up 2.92E-04 3.58 Up 1.07E-02 3.11 Up 1.62E-06 CRNDE 3.89 Up 2.17E-04 3.70 Up 1.45E-04 2.55 Up 9.98E-03 1.58 Up 3.61E-04 AATBC 3.74 Up 4.11E-06 2.16 Up 4.04E-03 1.43 Up 6.12E-03 1.32 Up 7.55E-03 UCA1 3.68 Up 9.30E-03 4.20 Up 8.42E-03 4.37 Up 2.63E-02 1.77 Up 1.19E-03 LINC00858 3.42 Up 5.97E-03 3.67 Up 3.03E-03 3.49 Up 3.27E-02 2.46 Up 1.34E-03 CASC19 3.31 Up 1.02E-05 2.81 Up 4.39E-04 2.66 Up 2.93E-02 3.83 Up 2.49E-07 LINC01132 2.31 Up 1.13E-03 1.92 Up 3.78E-02 2.07 Up 8.07E-04 1.23 Up 1.90E-03 OGFRP1 2.30 Up 8.13E-04 2.00 Up 1.64E-02 1.49 Up 1.37E-02 0.89 Up 2.65E-02 SNHG1 2.21 Up 5.63E-02 2.51 Up 3.40E-02 1.13 Up 9.53E-03 0.62 Up 3.84E-02 PVT1 2.09 Up 9.43E-05 2.45 Up 2.28E-05 2.52 Up 9.90E-05 1.32 Up 1.51E-03 CASC8 1.95 Up 2.49E-02 3.77 Up 8.84E-05 2.67 Up 9.03E-03 0.98 Up 4.94E-02 SLCO4A1-AS1 1.95 Up 3.90E-04 2.92 Up 2.50E-02 3.15 Up 6.34E-04 2.16 Up 2.88E-03 FOXP4-AS1 1.90 Up 2.00E-03 2.64 Up 1.68E-05 1.70 Up 7.04E-03 2.31 Up 3.77E-05 SNHG3 1.73 Up 1.06E-03 1.11 Up 1.97E-03 1.28 Up 5.67E-03 0.91 Up 1.86E-02 LINC01315 1.72 Up 1.76E-02 1.74 Up 3.06E-03 2.20 Up 1.64E-03 1.59 Up 3.38E-04 NKILA 1.63 Up 3.37E-02 3.26 Up 5.30E-03 2.27 Up 4.05E-03 1.79 Up 6.82E-03 RHPN1-AS1 1.61 Up 4.09E-02 2.44 Up 1.06E-03 1.89 Up 1.01E-02 1.60 Up 5.23E-03 LINC01811 1.58 Up 1.78E-02 3.65 Up 5.55E-04 3.76 Up 3.18E-03 1.40 Up 3.26E-02 BOK-AS1 1.58 Up 4.17E-02 2.22 Up 9.33E-03 1.52 Up 9.35E-03 1.58 Up 4.74E-03 PLAC4 1.54 Up 2.23E-03 1.63 Up 9.39E-03 1.38 Up 4.47E-02 0.92 Up 3.64E-02 LUCAT1 1.48 Up 5.42E-02 2.44 Up 2.49E-03 3.27 Up 5.16E-03 1.49 Up 9.42E-03 DDX11-AS1 1.27 Up 1.07E-02 1.43 Up 2.99E-04 1.07 Up 3.54E-02 1.90 Up 5.28E-05 SNHG16 1.16 Up 3.59E-02 0.72 Up 4.71E-02 1.51 Up 4.90E-04 1.01 Up 1.41E-02 VPS9D1-AS1 1.13 Up 1.84E-03 1.20 Up 1.99E-02 1.41 Up 7.74E-03 1.70 Up 4.02E-04 TDRKH-AS1 0.97 Up 6.66E-04 1.20 Up 1.12E-03 1.15 Up 4.16E-02 1.31 Up 9.61E-04 ZFAS1 0.88 Up 8.16E-02 1.00 Up 4.14E-03 1.60 Up 2.97E-04 1.22 Up 5.20E-04 SNHG17 0.81 Up 5.95E-02 1.70 Up 3.08E-04 2.03 Up 3.46E-03 1.18 Up 4.40E-03 LINC02000 -1.16 Down 8.17E-02 -1.48 Down 2.17E-02 -2.43 Down 4.96E-03 -1.52 Down 1.25E-03 SEMA6A-AS2 -1.35 Down 1.04E-02 -2.94 Down 5.38E-04 -2.12 Down 4.48E-03 -1.49 Down 2.34E-03 TARID -1.41 Down 3.80E-02 -1.37 Down 3.25E-02 -2.17 Down 8.40E-04 -1.08 Down 3.09E-03 FAM95B1 -1.57 Down 1.15E-02 -2.48 Down 5.30E-03 -1.95 Down 1.32E-02 -1.26 Down 1.57E-03 LINC01954 -1.78 Down 1.35E-02 -1.98 Down 7.29E-03 -1.84 Down 1.85E-02 -1.07 Down 1.34E-02 PGM5-AS1 -2.90 Down 1.12E-04 -2.01 Down 4.79E-02 -2.08 Down 2.68E-02 -3.38 Down 1.63E-03 CDKN2B-AS1 -3.65 Down 6.18E-03 -3.02 Down 1.49E-02 -2.20 Down 9.41E-03 -3.11 Down 2.01E-04 3.2 Validation of the diagnostic potential of the lncRNAs To evaluate the diagnostic potential of these lncRNAs, we quantified their expression levels in plasma samples from 22 HC, 49 NAA, 24 AA and 52 CRC patients using qRT-PCR. The baseline clinical characteristics of all enrolled participants are summarized in Table 2 . Giving the low abundance of lncRNA in circulation[ 22 ], we prioritized the 30 up-regulated lncRNAs for experimental validation. Table 2 Baseline Characteristics of the Study Participants Characterastics HC (n = 21) NAA (n = 49) AA (n = 24) CRC (n = 52) Age,years 45 (31–56) 60 (54–65) 53.8(57.5–64.8) 61(56.75–69.25) Male,n(%) 9 (42.9) 26 (53.1) 16 (66.7) 27(51.9) BMI,kg/m2 22.63 (21.77–24.34) 24.01 (21.46–26.12) 23.96 (21.42–24.98) 22.77 (20.62–24.63) CEA, ng/mL 1.29 (1.68–1.91) 2.11 (1.63–3.03) 2.40 (1.66–3.96) 4.20 (2.02–7.90) Location Left Colon - 27 (55.1) 18 (75.0) 37(78.3) Right Colon - 22 (44.9) 6 (25.0) 25(48.1) AJCC I + II - - - 29(55.8) III + IV - - - 23(44.2) NAA: Non-advanced adenoma. AA: Advanced adenoma. CRC: Colorectal Cancer. HC: Healthy control. Four lncRNAs—FOXP4-AS1, CRNDE, UCA1, and SNHG17—demonstrated stable detection and distinct dysregulation patterns in plasma (Fig. 2 a). FOXP4-AS1 showed significant up-regulation in both NAA (p = 0.038) and CRC (p = 0.016) groups compared to HC. CRNDE was elevated exclusively in CRC samples (p = 0.019), while both UCA1 and SNHG17 were significantly up-regulated in NAA (UCA1, p = 0.009; SNHG17, p = 0.039), AA (p = 0.001 for both), and CRC (p < 0.001 for both) compared to HC. These findings were validated in both GEO datasets and our in-house RNA-seq data. Consistent with plasma data, all four lncRNAs exhibited significant up-regulation in lesion tissues compared to normal controls (Fig. 2 b). 3.3 Diagnostic Value of the lncRNA Biomarkers In distinguishing NAA from HC, FOXP4-AS1 achieved an AUC of 0.683 (95% CI: 0.548–0.818; p = 0.016), UCA1 showed an AUC of 0.730 (95% CI: 0.601–0.858; p = 0.002), and SNHG17 demonstrated an AUC of 0.723 (95% CI: 0.599–0.847; p = 0.003) (Fig. 3 a). For AA versus HC, FOXP4-AS1 yielded an AUC of 0.706 (95% CI: 0.552–0.860; p = 0.018), while UCA1 and SNHG17 showed higher AUC values of 0.835 (95% CI: 0.717–0.953; p < 0.001) and 0.812 (95% CI: 0.688–0.935; p < 0.001), respectively (Fig. 3 b). In the CRC versus HC, FOXP4-AS1 attained an AUC of 0.707 (95% CI: 0.580–0.834; p = 0.006), CRNDE showed an AUC of 0.689 (95% CI: 0.553–0.824; p = 0.012), UCA1 reached an AUC of 0.865 (95% CI: 0.777–0.954; p < 0.001), and SNHG17 had the highest AUC of 0.881 (95% CI: 0.805–0.957; p < 0.001) (Fig. 3 c). When evaluating all leision cases versus HC, FOXP4-AS1 produced an AUC of 0.698 (95% CI: 0.582–0.813; p = 0.004), while both UCA1 and SNHG17 showed AUCs of 0.806 (95% CI: 0.712–0.901; p < 0.001) and 0.806(95% CI: 0.722–0.889; p < 0.001), respectively (Fig. 3 d). ROC analysis for distinguishing non-advanced adenoma from advanced lesions (including AA and CRC) showed that UCA1 had an AUC of 0.637 (95% CI: 0.535–0.739; p = 0.010) and SNHG17 achieved an AUC of 0.704 (95% CI: 0.613–0.795; p < 0.001) (Fig. 3 e). 3.4 Diagnostic Value of the Combined lncRNA To improve diagnostic performance, we constructed various combinations of the candidate lncRNAs. In distinguishing NAA from HC, multiple two-lncRNA and three-lncRNA combinations achieved AUC values ranging from 0.727 to 0.734 (all p < 0.01), with the FOXP4-AS1 + SNHG17 combination showing the highest AUC of 0.734 (95% CI: 0.610–0.858, p = 0.002) (Fig. 3 f). For AA versus HC, three-lncRNA combinations demonstrated superior performance. The FOXP4-AS1 + UCA1+SNHG17 (FUS) combination achieved the highest AUC of 0.861 (95% CI: 0.757–0.966, p < 0.001), followed by other combinations with AUCs ranging from 0.819 to 0.841(Fig. 3 g). In CRC diagnosis, the FUS panel attained an AUC of 0.906 (95% CI: 0.838–0.973, p < 0.001), while other combinations achieving AUCs between 0.892 and 0.904 (Fig. 3 h). When detecting any leisions versus HC, the FUS combination maintained strong performance with an AUC of 0.824 (95% CI: 0.744–0.905, p < 0.001), while other combinations produced AUC values between 0.806 and 0.814 (Fig. 3 i). For differentiating NAA from advanced lesions, CRNDE+UCA1 + SNHG17 (CUS) combination showed the best performance with an AUC of 0.716 (95% CI: 0.625–0.807, p < 0.001), and other combinations achieved AUC values ranging from 0.706 to 0.713 (Fig. 3 j). 3.5 Diagnostic value of lncRNAs with conventional biomarkers We further assessed whether combining lncRNA biomarkers with carcinoembryonic antigen (CEA) could enhance diagnostic performance. For NAA versus HC, CEA alone showed limited diagnostic value with an AUC of 0.665 (95% CI: 0.530–0.800; p = 0.029). However, when combined with lncRNA panels such as FUS + CEA and UCA1 + SNHG17 (US) + CEA both achieved the highest AUC of 0.81 (95% CI: 0.697–0.924 and 0.696–0.923, respectively). Other combinations including FOXP4-AS1 + UCA1 (FU) + CEA and FOXP4-AS1 + SNHG17 (FS) + CEA also showed improved AUC values ranging from 0.79 to 0.798 (Fig. 4 a and d). The integration of lncRNA biomarkers with CEA demonstrated remarkable improvement in detecting AA versus HC. While CEA alone showed limited diagnostic value with an AUC of 0.695 (95% CI: 0.532–0.859; p = 0.025), combination with lncRNA panels significantly enhanced diagnostic performance. The three-lncRNA panel FUS + CEA achieved the highest AUC of 0.893 (95% CI: 0.803–0.983). Other combinations including US + CEA (AUC = 0.889), FS + CEA (AUC = 0.883) and FU + CEA (AUC = 0.863) all showed substantially improved diagnostic accuracy compared to CEA alone (Fig. 4 b and e). We further evaluated whether integrating the lncRNA biomarkers with the conventional tumor marker CEA could enhance CRC diagnostic performance. While CEA alone showed an AUC of 0.789 (95% CI: 0.686–0.893; p < 0.001), combining CEA with the three-lncRNA panel FUS significantly improved the diagnostic accuracy, achieving the highest AUC of 0.950 (95% CI: 0.898-1.000; p < 0.001) (Fig. 4 c and f). For detecting all lesions versus normal controls, CEA alone showed an AUC of 0.723 (95% CI: 0.626–0.821; p = 0.001). The combination of CEA with various three-lncRNA panels significantly improved diagnostic accuracy, with FU + CEA achieving the highest AUC of 0.873 (95% CI: 0.795–0.952; p < 0.001), while FUS + CEA and US + CEA both reached AUCs of 0.870 (Fig. 4 g). For distinguishing advanced lesions from non-advanced adenoma, CEA alone demonstrated limited value with an AUC of 0.669 (95% CI: 0.575–0.762; p = 0.002). Integration of CEA with lncRNA panels markedly enhanced diagnostic performance, with US + CEA showing the best performance (AUC = 0.780, 95% CI: 0.699–0.860; p < 0.001), followed closely by CUS + CEA and CRNDE+SNHG17 + CEA (AUCs = 0.778 and 0.776, respectively) (Fig. 4 h). 3.6 Correlation with Clinicopathological Features The relationships between the expression levels of the four candidate lncRNAs and the clinicopathological features of CRC patients were analyzed (Table 3 ). No significant associations were observed between FOXP4-AS1, UCA1, or SNHG17 expression and age, gender, BMI, CEA level, tumor differentiation, AJCC stage, or tumor location. Table 3 Relationship between clinicopathological characteristics of lncRNAs for colorectal cancer patients. Variable Cases FOXP4-AS1 P -value CRNDE P -value UCA1 P -value SNHG17 P -value Median (IQR) Median (IQR) Median (IQR) Median (IQR) Age 0.646 0.095 0.700 0.322 < 60 24 7.494(6.240–8.720) 3.183(2.082–4.021) 5.406(3.242–6.604) 6.735(4.336–7.856) ≧ 60 28 7.156(5.526–8.088) 4.790(2.645–7.873) 5.248(3-284-6.421) 5.481(3.613–7.286) Gerder 0.336 0.405 0.447 Male 27 7.061(6.602–8.623) 3.609(2.432–6.514) 5.251(3-369-6.220) 6.575(5.236–7.534) 0.178 Female 25 7.185(5-170-8.083) 3.328(1.394–6.338) 5.5.837(3.228–6.913) 5.406(2.685–8.203) BMI 0.181 0.034 0.084 0.884 < 24 35 7.161(4.997–8.168) 3.024(1.301–5.670) 4.732(2.331–6.397) 6.389(3.605–7.637) ≧ 24 17 7.919(7.042–8.715) 4.935(3.233–7.858) 5.756(5.149–6.491) 6.374(5.034–7.581) CEA, ng/mL 0.374 0.897 0.182 0.085 < 3.4 22 7.142(5.310–8.082) 3.869(1.539–7.432) 5.089(2.498–5.947) 5.111(2.595–7.236) ≧ 3.4 30 7.703(6.356-8.600) 3.353(2.466–6.297) 5.797(3.627–6.801) 6.509(5.127–7.991) Differentiation 0.820 0.916 0.329 0.529 Well 7 7.185(6.300-7.732) 6.338(0.373–8.125) 6.415(4.077–8.748) 6.812(4.045–10.028) Moderate+Poor 45 7.548(5.732–8.531) 3.377(2.426–6.153) 5.251(3.228–6.378) 5.374(3.620–7.486) AJCC stage 0.525 0.692 0.107 0.182 I + II 23 7.100(5-451-8.410) 3.377(1.685–6.165) 4.732(2.253–6.252) 5.034(3.096–7.534) III + IV 29 7.601(6.161–8.252) 3.609(2.426–7.655) 5.741(4.033–7.028) 6.442(5.406–8.059) Location 0.976 0.007 0.739 0.739 Left colon 37 7.212(5.732–8.531) 3.257(1.976–7.655) 5.251(3.247–6.913) 6.442(3.573–8.059) Right colon 15 7.774(5.492–8.068) 7.774(5.492–8.068) 5.310(3.569–6.252) 6.336(4.245–7.129) IQR: Interquartile range. However, Higher CRNDE levels were associated with BMI ≥ 24 (p = 0.034) and right-sided colon tumors (p = 0.007). 4 Discussion In this study, we identified and validated several lncRNAs—FOXP4-AS1, CRNDE, UCA1, and SNHG17—as promising diagnostic biomarkers for CRC and its precursors. Through integrated bioinformatics analysis, qRT-PCR validation, and ROC curve analysis, we demonstrated that these lncRNAs exhibited significant dysregulation in plasma samples from patients with adenoma and CRC. The diagnostic performance of these lncRNAs, especially when combined with CEA, highlights their potential for early, non-invasive CRC detection. Unlike most prior blood-based CRC biomarker studies that focused solely on CRC diagnosis, our study specifically included plasma samples from patients with precancerous lesions—both NAA and AA—allowing us to explore lncRNAs as potential biomarkers for identifying adenomas with high malignant transformation risk. This addresses a critical gap in CRC screening: the early, non-invasive identification of patients who are likely to progress from benign adenomas to malignant disease. The stage-specific dysregulation patterns of these lncRNAs offer insights into the molecular progression of CRC. UCA1 and SNHG17 were upregulated from the NAA stage, suggesting their potential as early indicators of adenoma development. In contrast, CRNDE was selectively elevated in CRC, supporting its role as a specific marker for malignant transformation. FOXP4-AS1 showed significant upregulation in both NAA and CRC, implicating it in both early tumorigenesis and advanced disease progression. Collectively, these findings position these lncRNAs not only as diagnostic markers but also as potential tools for monitoring disease progression and stratifying patient risk. Mechanistically, these lncRNAs have been previously implicated in tumor biology[ 23 – 25 ]. FOXP4-AS1 promotes oncogenesis by acting as a competing endogenous RNA (ceRNA), silencing tumor suppressor genes via epigenetic regulation, and enhancing immune evasion pathways[ 26 – 29 ]. CRNDE has been linked to altered cellular metabolism and immune escape in CRC[ 30 – 32 ], while UCA1 activates critical signaling pathways such as MAPK/JNK and Wnt/β-catenin[ 33 – 36 ]. SNHG17, a member of the small nucleolar RNA host gene family, contributes to tumor proliferation via suppression of p57 and alternative splicing regulation by RBM10[ 37 , 38 ]. The dysregulation of these lncRNAs in our plasma cohort is thus consistent with their established roles in CRC pathogenesis. Our results delineate the diagnostic utility of these markers across clinically relevant scenarios. For instance, FOXP4-AS1 maintained a moderate but consistent AUC (0.683–0.707) across all stages versus healthy controls, suggesting its role as a ubiquitous but non-specific marker of early neoplasia. In stark contrast, UCA1 and SNHG17 demonstrated a marked and statistically significant increase from the non-advanced adenoma stage (UCA1: AUC = 0.730; SNHG17: AUC = 0.723) to the advanced adenoma stage (UCA1: AUC = 0.835, p < 0.001; SNHG17: AUC = 0.812, p < 0.001), and reached even higher values in CRC. This graduated enhancement in accuracy parallels the advancing genomic and phenotypic instability along the adenoma-carcinoma sequence, implying that the plasma levels of UCA1 and SNHG17 may reflect increasing tumor burden or specific aggressive biological features. Notably, SNHG17 demonstrated particular value in the clinically challenging task of risk stratification, differentiating non-advanced adenomas from the combined group of advanced lesions (AA and CRC) with an AUC of 0.704. This capability positions SNHG17 not merely as a detection tool, but as a potential triage biomarker to identify, within the adenoma pool, those patients harboring or at immediate risk for high-grade neoplasia, thereby informing personalized surveillance intensity. As anticipated, the integration of multiple lncRNAs into combinatorial panels substantially outperformed any single marker. The three-lncRNA panel FUS achieved excellent diagnostic accuracy for CRC (AUC = 0.906) and AA (AUC = 0.861). More importantly, the differential efficacy of specific combinations for distinct clinical questions highlights the principle of precision biomarker application. The panel CUS, for example, was optimized for distinguishing advanced from non-advanced lesions (AUC = 0.716), a task crucial for risk stratification but poorly served by conventional methods. This suggests that tailored biomarker signatures can be developed to answer specific clinical questions, moving beyond a one-size-fits-all diagnostic test. The most impactful finding from this study is the remarkable synergy between the novel lncRNA panels and the conventional serum marker CEA. While CEA alone showed limited accuracy for detecting adenomas (AUC = 0.695 for AA vs. HC; AUC = 0.665 for NAA vs. HC), combining it with the FUS panel greatly enhanced performance, yielding outstanding results (AUC = 0.950 for CRC; 0.893 for AA). Notably, the FUS + CEA panel could distinguish any lesion from healthy controls with an AUC of 0.873 makes it a strong candidate for a high-performance, blood-based screening test. This combination not only addresses a significant gap in current screening methods by improving sensitivity for the critical pre-cancerous stage where CEA alone falls short, but it also achieves near-perfect discrimination for CRC. We also examined the correlation between lncRNA expression and clinicopathological features in CRC patients. While no significant associations were found between FOXP4-AS1, UCA1, or SNHG17 expression and factors such as age, gender, or tumor differentiation, higher levels of CRNDE were associated with BMI ≥ 24 and right-sided colon tumors. This suggests that CRNDE may serve as a useful biomarker for identifying specific subtypes of CRC, such as those with a higher metabolic burden or those located in the right colon. These findings warrant further investigation to explore the potential clinical implications of CRNDE in CRC prognosis. Liquid biopsy has emerged as a crucial non-invasive tool for early cancer screening due to its convenience and high sensitivity[ 39 ]. By analyzing biomarkers such as circulating tumor DNA (ctDNA), miRNA, lncRNA, and exosomes in plasma, liquid biopsy can provide diagnostic information at the early stages of cancer, especially for detecting CRC and its precursors (such as advanced adenomas), showing immense potential[ 40 ]. For example, in stool-based studies, miR-21 and miR-106a have been identified as significantly overexpressed in individuals with CRC and advanced adenomas, demonstrating the potential of miRNA as a non-invasive biomarker for early detection[ 41 ]. Meanwhile, ctDNA has been widely used in post-surgical monitoring and minimal residual disease detection[ 42 , 43 ]. Additionally, methylated DNA markers such as SEPT9, BMP3, and NDRG4 have been incorporated into both blood and stool tests to improve sensitivity for advanced adenomas[ 44 , 45 ]. However, the clinical translation of liquid biopsy still faces technical and economic challenges. Firstly, the extraction of biomarkers still requires complex technical support, particularly regarding the purity and cost issues of extracting exosomes and miRNA[ 46 ], which limits their widespread application in larger populations. Furthermore, although miRNA and lncRNA biomarkers have shown high sensitivity in studies, their consistency and reproducibility across different patient populations still need further validation[ 47 ]. While current research on liquid biopsy biomarkers primarily focuses on early diagnosis and risk stratification, integrating them into clinical practice still requires addressing issues such as biomarker standardization, platform differences, and cost-effectiveness[ 48 , 49 ]. Therefore, future research should not only focus on large-scale, multi-center validation but also work on improving technology and reducing costs to promote the widespread application of liquid biopsy in early CRC screening. Despite the promising results, there are still some limitations in this work. First, the sample size was relatively small and all samples were collected from a single center, which may affect the generalizability of the findings. Future studies involving larger, multi-center cohorts are needed to validate these results. Second, the lack of longitudinal data limits our understanding of the prognostic value of these lncRNAs. Long-term follow-up studies are necessary to evaluate their association with disease progression, recurrence, and survival. Additionally, while we identified several lncRNAs with diagnostic potential, their functional roles in CRC remain unclear. Further mechanistic studies are required to elucidate how these lncRNAs influence tumor development and progression. Finally, the cost-effectiveness and practical implementation of lncRNA-based tests in routine clinical settings need to be thoroughly evaluated. While they represent a promising non-invasive alternative, further studies are needed to assess their cost-benefit ratio, scalability, and seamless integration into existing clinical workflows before widespread adoption can be achieved. In conclusion, our study identifies FOXP4-AS1, CRNDE, UCA1, and SNHG17 as potential plasma-based biomarkers for the early detection of CRC and adenomas. These lncRNAs, particularly when combined into multi-lncRNA panels, offer a promising, non-invasive approach for CRC screening and diagnosis. Their integration with conventional biomarkers such as CEA could further improve diagnostic accuracy, making these panels an attractive alternative for non-invasive, early detection of colorectal cancer in clinical settings. Abbreviations CRC Colorectal cancer lncRNA Long non-coding RNA NAA Non-advanced adenoma AA Advanced adenoma HC Healthy control qRT-PCR Quantitative real-time polymerase chain reaction SD Standard deviation ROC Receiver operating characteristic AUC Area under the curve CI Confidence interval CEA Carcinoembryonic antigen FUS FOXP4-AS1 + UCA1 + SNHG17 CUS CRNDE + UCA1 + SNHG17 US UCA1 + SNHG17 FU FOXP4-AS1 + UCA1 FS FOXP4-AS1 + SNHG17 AJCC American Joint Committee on Cancer ceRNA Competing endogenous RNA ctDNA Circulating tumor DNA Declarations Ethics approval and consent to participate: Written informed consent was obtained from each individual participant, and the experimental protocol was approved by the West China Hospital Institutional Review Board (NO.2021 − 179). Also all experiments were performed in accordance with relevant guidelines and regulations of the Declaration of Helsinki. Funding: This work as supported by the Natural Science Foundation of Sichuan Science and Technology Department (2025ZNSFSC1897 and 2025ZNSFSC1899). Acknowledgements: None. Author Contribution XB and QL made substantial contributions to the conception and design of the work, material preparation, data collection, and analysis. They wrote the first draft of the manuscript. LM and KD contributed to data collection and analysis. XL and JY supervised the work, contributed to the interpretation of data, and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Data Availability The raw RNA-seq data generated during the current study are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA690126. Publicly available datasets analysed during the current study include GSE164541 and GSE109203. References Siegel, R. 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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-9169352","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":614630361,"identity":"7960574e-2332-4f9a-8ee6-a13eb51e8288","order_by":0,"name":"Xiaoyi Bai","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Bai","suffix":""},{"id":614630362,"identity":"2f23a30e-1bd1-4a40-bd56-0e4e30316cb3","order_by":1,"name":"Qing Lu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Lu","suffix":""},{"id":614630363,"identity":"cfe47c11-ab7b-49e3-a368-3d3b1c10e68f","order_by":2,"name":"Lihong Ma","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Ma","suffix":""},{"id":614630364,"identity":"5dc4d5fe-8a1b-4739-987a-aa96aa7007f9","order_by":3,"name":"Kai Deng","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Deng","suffix":""},{"id":614630367,"identity":"e3c46cc2-cc71-495a-88e1-e125bc6102d4","order_by":4,"name":"Xiuhe Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPiA+wNjAIMfAwEOkFjaoFmPStDAAtSQ2EK9FIsfwMO8Om/QNx88e/vCzjUHenLCWtITDvGfScjecyUuT7G1jMNzZQEgLz+EDh3nbDuduu8FjxszYxpBgcICgloMNQC3/081u8Bh/Jk4LezPIlgMJQC0G0kRqaUs4OLct2XD/mRwzyZ5zEoYbCGnhZ+Yx/vC2zU5esv2M8YcfZTbyBG1BBxIkqh8Fo2AUjIJRgBUAAKz/PYGTQCULAAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xiuhe","middleName":"","lastName":"Lv","suffix":""},{"id":614630370,"identity":"e23569d5-cf24-4bbc-808b-c13e7f16cd22","order_by":5,"name":"Jinlin Yang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jinlin","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-03-19 12:11:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105984340,"identity":"e02cb26c-6e74-4b8a-be8f-d9711c338b61","added_by":"auto","created_at":"2026-04-02 07:13:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1377570,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed genes in the three datasets and the results of their intersection.\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of the differentially expressed genes for GSE164541, GSE109203 and in-house sequencing data. B. Venn diagram for the overlap of up- and down-regulated genes.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/040619a604e3b0e33f2d10ec.png"},{"id":105984358,"identity":"1151c168-9d7d-469d-b75b-571d20eed947","added_by":"auto","created_at":"2026-04-02 07:13:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1403254,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of FOXP4-AS1, CRNDE, UCA1 and SNHG17 in plasma and three datasets.\u003c/p\u003e\n\u003cp\u003eA. The scatter plots displaying the plasma expression levels of the four lncRNAs in healthy controls (HC), non-advanced adenoma (NAA), advanced adenoma (AA), and colorectal cancer (CRC) patients; B. The relative expression of the four lncRNAs in GSE164541, GSE109203 and in-house sequencing data.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/a7ec0f785023c8933f67a202.png"},{"id":105984342,"identity":"24ed1176-70a2-4e45-a027-726ab9e4a381","added_by":"auto","created_at":"2026-04-02 07:13:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1160445,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic value of individual and combined plasma lncRNAs.\u003c/p\u003e\n\u003cp\u003eA. ROC curves for the four lncRNAs in distinguishing non-advanced adenoma (NAA) from healthy controls (HC); B. ROC curves the four lncRNAs in distinguishing advanced adenoma (AA) from HC; C. ROC curves for the four lncRNAs in distinguishing colorectal cancer (CRC) from HC; D. ROC curves of the four lncRNAs in distinguishing all lesions from normal controls; E. ROC curves for the four lncRNAs in distinguishing NAA from advanced lesions; F. ROC curves of the top four combinations for distinguishing NAA; G. ROC curves of the top four combinations for AA; H. ROC curves of the top four combinations for CRC; I. ROC curves of the top four combinations for detecting any lesions from normal controls; J. ROC curves of the top four combinations for differentiating NAA from advanced lesions.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/2165a883c1aa99dbba11988e.png"},{"id":105984343,"identity":"6012b348-99b6-4a89-94e5-f3b670f8f7fa","added_by":"auto","created_at":"2026-04-02 07:13:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":918092,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic value of CEA alone and in combination with lncRNA panels for differential diagnosis and lesion stratification.\u003c/p\u003e\n\u003cp\u003eA.The scatter plots displaying the plasma expression levels of the CEA in non-advanced adenoma (NAA); B.The scatter plots displaying the plasma expression levels of the CEA in advanced adenoma (AA); C.The scatter plots displaying the plasma expression levels of the CEA in colorectal cancer (CRC); D. ROC curves for CEA alone and the top four lncRNA panels combined with CEA in distinguishing NAA from healthy controls (HC); E. ROC curves for CEA alone and the top four lncRNA panels combined with CEA in distinguishing AA from healthy controls (HC); F. ROC curves for CEA alone and the top four lncRNA panels combined with CEA in distinguishing CRC from HC; G. ROC curves for CEA alone and the top four lncRNA panels combined with CEA in distinguishing any leisions from HC; H. ROC curves for CEA alone and the top four lncRNA panels combined with CEA in distinguishing NAA from advanced lesions.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/316de9ed615dfc2195a13301.png"},{"id":106093825,"identity":"8d4e9444-1bf9-4019-9745-7f300e1d5c9e","added_by":"auto","created_at":"2026-04-03 11:39:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6016070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/e51d9d68-04fc-4f2e-a09f-6b0f1abdfb09.pdf"},{"id":105984371,"identity":"42f6d9e5-1230-4fa6-952a-6d1a3d4b6383","added_by":"auto","created_at":"2026-04-02 07:13:48","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11593,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9169352/v1/4b742b12690c7bf53da8e56c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating lncRNAs as Early Diagnostic Biomarkers for Colorectal Cancer: A case-control diagnostic accuracy study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early detection is pivotal for improving prognosis and reducing disease burden, yet CRC often remains asymptomatic until advanced stages[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although colonoscopy remains the gold standard for early CRC detection, its invasiveness and high cost limit its widespread application in population-based screening programs[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, circulating biomarkers obtained through liquid biopsies have emerged as promising non-invasive alternatives for cancer detection[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In particular, long non-coding RNAs (lncRNAs)\u0026mdash;RNA transcripts longer than 200 nucleotides that are not translated into proteins\u0026mdash;are increasingly recognized for their regulatory roles in cancer biology[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Aberrant expression of lncRNAs has been implicated in numerous cellular processes, including proliferation, apoptosis, metabolism, and differentiation[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Importantly, circulating lncRNAs, detectable in body fluids such as plasma, have thus gained significant attention as novel diagnostic and prognostic markers in various malignancies, including CRC[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile most previous studies on CRC blood-based biomarkers have focused primarily on distinguishing CRC patients from healthy individuals[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], there remains a critical unmet need for early-stage risk stratification\u0026mdash;particularly the identification of high-risk precancerous lesions. The majority of sporadic CRCs arise through the conventional adenoma\u0026ndash;carcinoma sequence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while approximately 25% follow the serrated neoplasia pathway[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Detecting precancerous lesions at different points along this progression, especially distinguishing advanced adenomas from non-advanced adenomas, is crucial for effective early intervention.\u003c/p\u003e \u003cp\u003eThis study extends previous work by incorporating plasma samples from both non-advanced and advanced adenoma patients, in addition to CRC and healthy controls. By integrating bioinformatics screening and experimental validation, we aim to identify circulating lncRNAs that exhibit progressive dysregulation along the adenoma\u0026ndash;carcinoma sequence. Our goal is not only to enable non-invasive early detection of CRC, but also to identify patients with adenomas at high risk of malignant transformation, providing a novel strategy for risk-based screening and personalized surveillance.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Candidate lncRNA Identification\u003c/h2\u003e \u003cp\u003eTo identify lncRNAs associated with the early development of CRC, we performed a systematic screening using both public datasets and in-house sequencing data. A search of the Gene Expression Omnibus (GEO) database was conducted with the keywords 'colorectal cancer OR adenoma' and 'RNA-Seq' to identify datasets profiling the normal-adenoma-carcinoma sequence. Two datasets, GSE164541 and GSE109203, were selected (details in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, whole-transcriptome sequencing was conducted on 5 pairs of matched CRC and adjacent normal tissues, and the lncRNA data were extracted for analysis.\u003c/p\u003e \u003cp\u003eThe criteria for identifying significant differential expression were as follows: for the Normal vs. Adenoma comparison in GSE164541, a threshold of |log2FC| \u0026ge; 1.5 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was applied. For all Normal vs. Carcinoma comparisons (in GSE164541, GSE102920, and our in-house sequencing data), a threshold of |log2FC| \u0026ge; 1.5 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used. The Sangerbox online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sangerbox.com/tool\u003c/span\u003e\u003cspan address=\"http://www.sangerbox.com/tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and \u0026ldquo;R\u0026rdquo; was employed for this data processing and differential gene analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Collection\u003c/h2\u003e \u003cp\u003eThis study collected plasma samples from West China Hospital of Sichuan University from 2021 to 2023. We recruited patients aged 18 to 75 years with a pathological diagnosis of colorectal adenoma or adenocarcinoma. Participants were categorized into the following groups based on colonoscopic and histopathological findings: (1) Non-advanced Adenoma Group (NAA): Patients with adenomas that did not meet the criteria for advanced adenoma. (2) Advanced Adenoma Group (AA): Patients with adenomas exhibiting at least one of the following high-risk features: size\u0026thinsp;\u0026ge;\u0026thinsp;10 mm, villous component\u0026thinsp;\u0026ge;\u0026thinsp;25%, or high-grade dysplasia[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (3) CRC Group: Patients with confirmed invasion of malignant cells through the muscularis mucosa. We also recruited a Healthy Control (HC) Group, comprised of individuals who underwent colonoscopy with normal findings, defined as no detection of adenomas, serrated polyps, or colorectal cancer.\u003c/p\u003e \u003cp\u003eThe exclusive criterion: (1) patients who received any type of treatment; (2) patients with a concurrent or previous diagnosis of any other type of cancer; (3) individuals with a history of acute clinical events (such as myocardial infarction within the past 6 months) or those with severe chronic comorbidities; (4) use of any anticoagulant or antiplatelet agent medications (such as aspirin or warfarin), or a diagnosed coagulation disorder. Written informed consent was obtained from each individual participant, and the experimental protocol was approved by the West China Hospital Institutional Review Board(NO.2021\u0026thinsp;\u0026minus;\u0026thinsp;179. Also all experiments were performed in accordance with relevant guidelines and regulations of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Processing\u003c/h2\u003e \u003cp\u003ePeripheral blood samples (15 mL per participant) were collected in EDTA-containing tubes and processed using a two-step centrifugation protocol. To prevent cell lysis, the samples were first centrifuged at 1,600 \u0026times; g for 10 minutes at 4\u0026deg;C. The supernatant was then carefully transferred and subjected to a second centrifugation at 16,000 \u0026times; g for 10 minutes at 4\u0026deg;C to remove cellular debris and fragments. The resulting cell-free plasma was aliquoted into RNase-free tubes, preserved with Trizol LS reagent, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction and further analysis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 RNA Isolation\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from plasma samples using TRIzol\u0026trade; LS Reagent (Thermo Fisher Scientific, USA) according to the manufacturer's protocol. RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Scientific, USA). Complementary DNA (cDNA) was synthesized from 1 \u0026micro;g of total RNA using the HiScript III RT SuperMix for qPCR (+\u0026thinsp;gDNA wiper) (Vazyme, China) on a SimpliAmp\u0026trade; Thermal Cycler (Thermo Fisher Scientific, USA) to eliminate genomic DNA contamination and ensure high-quality reverse transcription.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Quantitative Real-Time PCR (qPCR)\u003c/h2\u003e \u003cp\u003eqPCR was performed using ChamQ SYBR Color qPCR Master Mix (Vazyme, China) on a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, USA). The thermal cycling protocol consisted of an initial denaturation at 95\u0026deg;C for 30 s, followed by 45 cycles of 95\u0026deg;C for 5 s and 60\u0026deg;C for 30 s. After evaluating the stability of candidate genes (GAPDH, β-actin, U6, and 5S rRNA), 5S rRNA was selected as the internal reference due to its superior stability in plasma samples. Primer sequences for target lncRNAs, designed to span exon regions, are detailed in Table S2. Assays with non-specific amplification, primer-dimer formation, or Ct values\u0026thinsp;\u0026gt;\u0026thinsp;35 were excluded. Relative expression levels were calculated using the ΔCt method, with a larger ΔCt value indicating lower relative expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS Statistics version 27.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism version 9.5.0 (GraphPad Software, San Diego, CA, USA). Normality was assessed using the Shapiro-Wilk test. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normally distributed data or as median with interquartile range [M (P25, P75)] for non-normally distributed data.\u003c/p\u003e \u003cp\u003eFor comparison of lncRNA expression across the four study groups, the Kruskal-Wallis test was employed followed by Dunn's post-hoc analysis for multiple comparisons. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were employed to assess the diagnostic value of the candidate biomarkers. A logistic regression model was developed to combine the identified lncRNAs with the conventional tumor marker CEA into a composite diagnostic panel.\u003c/p\u003e \u003cp\u003eAssociations between lncRNA expression levels and clinicopathological characteristics were analyzed using the Mann-Whitney U test. P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Candidate lncRNAs\u003c/h2\u003e \u003cp\u003eVolcano plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea illustrate the expression patterns of candidate lncRNAs. By integrating bioinformatics analysis of public datasets and in-house sequencing data, we identified 37 lncRNAs (30 up-regulated and 7 down-regulated) that were consistently dysregulated in both adenoma and carcinoma compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Detailed data are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLncRNAs expression in three datasets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLncRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGSE164541 A vs N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eGSE164541 T vs N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eGSE1029203 T vs N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eIn-house RNA-seq T vs N\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDescription\u003c/p\u003e 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colname=\"c10\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.51E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.18E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGATA2-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.96E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.78E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.76E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.99E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLACAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.42E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.92E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.62E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRNDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.45E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.98E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.61E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAATBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.04E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.12E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e7.55E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.30E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.42E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.63E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.19E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC00858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.97E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.03E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.27E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.34E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASC19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.39E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.93E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.49E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.78E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.07E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.90E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGFRP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.13E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.64E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.37E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.65E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.63E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.40E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.53E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.84E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.43E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.28E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.90E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.51E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASC8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.84E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.03E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.94E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLCO4A1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.90E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.50E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.34E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.88E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXP4-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.68E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.04E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.77E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.67E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.86E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.06E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.64E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.38E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNKILA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.30E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.05E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e6.82E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRHPN1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.09E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.01E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5.23E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.55E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.18E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.26E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOK-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.17E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.33E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.35E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.74E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.39E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.47E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.64E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUCAT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.42E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.49E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.16E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9.42E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDX11-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.99E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.54E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5.28E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.59E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.71E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.90E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.41E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVPS9D1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.99E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.74E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.02E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDRKH-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.66E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.12E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.16E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9.61E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZFAS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.16E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.14E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.97E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5.20E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNHG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.08E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.46E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4.40E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC02000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.17E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.17E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.96E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.25E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEMA6A-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.38E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.48E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.34E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTARID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.80E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.25E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.40E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e3.09E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAM95B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.30E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.32E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.57E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINC01954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.29E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.85E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.34E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM5-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.79E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.68E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e1.63E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDKN2B-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.18E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.49E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.41E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e2.01E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Validation of the diagnostic potential of the lncRNAs\u003c/h2\u003e \u003cp\u003eTo evaluate the diagnostic potential of these lncRNAs, we quantified their expression levels in plasma samples from 22 HC, 49 NAA, 24 AA and 52 CRC patients using qRT-PCR. The baseline clinical characteristics of all enrolled participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Giving the low abundance of lncRNA in circulation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], we prioritized the 30 up-regulated lncRNAs for experimental validation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacterastics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHC (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNAA (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAA (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCRC (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e45 (31\u0026ndash;56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e60 (54\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e53.8(57.5\u0026ndash;64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e61(56.75\u0026ndash;69.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e26 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e16 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e27(51.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI,kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22.63 (21.77\u0026ndash;24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e24.01 (21.46\u0026ndash;26.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.96 (21.42\u0026ndash;24.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e22.77 (20.62\u0026ndash;24.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.29 (1.68\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.11 (1.63\u0026ndash;3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.40 (1.66\u0026ndash;3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.20 (2.02\u0026ndash;7.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft Colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e37(78.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight Colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e25(48.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e29(55.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e23(44.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNAA: Non-advanced adenoma. AA: Advanced adenoma. CRC: Colorectal Cancer. HC: Healthy control.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFour lncRNAs\u0026mdash;FOXP4-AS1, CRNDE, UCA1, and SNHG17\u0026mdash;demonstrated stable detection and distinct dysregulation patterns in plasma (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). FOXP4-AS1 showed significant up-regulation in both NAA (p\u0026thinsp;=\u0026thinsp;0.038) and CRC (p\u0026thinsp;=\u0026thinsp;0.016) groups compared to HC. CRNDE was elevated exclusively in CRC samples (p\u0026thinsp;=\u0026thinsp;0.019), while both UCA1 and SNHG17 were significantly up-regulated in NAA (UCA1, p\u0026thinsp;=\u0026thinsp;0.009; SNHG17, p\u0026thinsp;=\u0026thinsp;0.039), AA (p\u0026thinsp;=\u0026thinsp;0.001 for both), and CRC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both) compared to HC.\u003c/p\u003e \u003cp\u003eThese findings were validated in both GEO datasets and our in-house RNA-seq data. Consistent with plasma data, all four lncRNAs exhibited significant up-regulation in lesion tissues compared to normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diagnostic Value of the lncRNA Biomarkers\u003c/h2\u003e \u003cp\u003eIn distinguishing NAA from HC, FOXP4-AS1 achieved an AUC of 0.683 (95% CI: 0.548\u0026ndash;0.818; p\u0026thinsp;=\u0026thinsp;0.016), UCA1 showed an AUC of 0.730 (95% CI: 0.601\u0026ndash;0.858; p\u0026thinsp;=\u0026thinsp;0.002), and SNHG17 demonstrated an AUC of 0.723 (95% CI: 0.599\u0026ndash;0.847; p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eFor AA versus HC, FOXP4-AS1 yielded an AUC of 0.706 (95% CI: 0.552\u0026ndash;0.860; p\u0026thinsp;=\u0026thinsp;0.018), while UCA1 and SNHG17 showed higher AUC values of 0.835 (95% CI: 0.717\u0026ndash;0.953; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.812 (95% CI: 0.688\u0026ndash;0.935; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn the CRC versus HC, FOXP4-AS1 attained an AUC of 0.707 (95% CI: 0.580\u0026ndash;0.834; p\u0026thinsp;=\u0026thinsp;0.006), CRNDE showed an AUC of 0.689 (95% CI: 0.553\u0026ndash;0.824; p\u0026thinsp;=\u0026thinsp;0.012), UCA1 reached an AUC of 0.865 (95% CI: 0.777\u0026ndash;0.954; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SNHG17 had the highest AUC of 0.881 (95% CI: 0.805\u0026ndash;0.957; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eWhen evaluating all leision cases versus HC, FOXP4-AS1 produced an AUC of 0.698 (95% CI: 0.582\u0026ndash;0.813; p\u0026thinsp;=\u0026thinsp;0.004), while both UCA1 and SNHG17 showed AUCs of 0.806 (95% CI: 0.712\u0026ndash;0.901; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.806(95% CI: 0.722\u0026ndash;0.889; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eROC analysis for distinguishing non-advanced adenoma from advanced lesions (including AA and CRC) showed that UCA1 had an AUC of 0.637 (95% CI: 0.535\u0026ndash;0.739; p\u0026thinsp;=\u0026thinsp;0.010) and SNHG17 achieved an AUC of 0.704 (95% CI: 0.613\u0026ndash;0.795; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Diagnostic Value of the Combined lncRNA\u003c/h2\u003e \u003cp\u003eTo improve diagnostic performance, we constructed various combinations of the candidate lncRNAs.\u003c/p\u003e \u003cp\u003eIn distinguishing NAA from HC, multiple two-lncRNA and three-lncRNA combinations achieved AUC values ranging from 0.727 to 0.734 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the FOXP4-AS1\u0026thinsp;+\u0026thinsp;SNHG17 combination showing the highest AUC of 0.734 (95% CI: 0.610\u0026ndash;0.858, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eFor AA versus HC, three-lncRNA combinations demonstrated superior performance. The FOXP4-AS1\u0026thinsp;+\u0026thinsp;UCA1+SNHG17 (FUS) combination achieved the highest AUC of 0.861 (95% CI: 0.757\u0026ndash;0.966, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by other combinations with AUCs ranging from 0.819 to 0.841(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eIn CRC diagnosis, the FUS panel attained an AUC of 0.906 (95% CI: 0.838\u0026ndash;0.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while other combinations achieving AUCs between 0.892 and 0.904 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh).\u003c/p\u003e \u003cp\u003eWhen detecting any leisions versus HC, the FUS combination maintained strong performance with an AUC of 0.824 (95% CI: 0.744\u0026ndash;0.905, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while other combinations produced AUC values between 0.806 and 0.814 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eFor differentiating NAA from advanced lesions, CRNDE+UCA1\u0026thinsp;+\u0026thinsp;SNHG17 (CUS) combination showed the best performance with an AUC of 0.716 (95% CI: 0.625\u0026ndash;0.807, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and other combinations achieved AUC values ranging from 0.706 to 0.713 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Diagnostic value of lncRNAs with conventional biomarkers\u003c/h2\u003e \u003cp\u003eWe further assessed whether combining lncRNA biomarkers with carcinoembryonic antigen (CEA) could enhance diagnostic performance. For NAA versus HC, CEA alone showed limited diagnostic value with an AUC of 0.665 (95% CI: 0.530\u0026ndash;0.800; p\u0026thinsp;=\u0026thinsp;0.029). However, when combined with lncRNA panels such as FUS\u0026thinsp;+\u0026thinsp;CEA and UCA1\u0026thinsp;+\u0026thinsp;SNHG17 (US)\u0026thinsp;+\u0026thinsp;CEA both achieved the highest AUC of 0.81 (95% CI: 0.697\u0026ndash;0.924 and 0.696\u0026ndash;0.923, respectively). Other combinations including FOXP4-AS1\u0026thinsp;+\u0026thinsp;UCA1 (FU)\u0026thinsp;+\u0026thinsp;CEA and FOXP4-AS1\u0026thinsp;+\u0026thinsp;SNHG17 (FS)\u0026thinsp;+\u0026thinsp;CEA also showed improved AUC values ranging from 0.79 to 0.798 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and d).\u003c/p\u003e \u003cp\u003eThe integration of lncRNA biomarkers with CEA demonstrated remarkable improvement in detecting AA versus HC. While CEA alone showed limited diagnostic value with an AUC of 0.695 (95% CI: 0.532\u0026ndash;0.859; p\u0026thinsp;=\u0026thinsp;0.025), combination with lncRNA panels significantly enhanced diagnostic performance. The three-lncRNA panel FUS\u0026thinsp;+\u0026thinsp;CEA achieved the highest AUC of 0.893 (95% CI: 0.803\u0026ndash;0.983). Other combinations including US\u0026thinsp;+\u0026thinsp;CEA (AUC\u0026thinsp;=\u0026thinsp;0.889), FS\u0026thinsp;+\u0026thinsp;CEA (AUC\u0026thinsp;=\u0026thinsp;0.883) and FU\u0026thinsp;+\u0026thinsp;CEA (AUC\u0026thinsp;=\u0026thinsp;0.863) all showed substantially improved diagnostic accuracy compared to CEA alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and e).\u003c/p\u003e \u003cp\u003eWe further evaluated whether integrating the lncRNA biomarkers with the conventional tumor marker CEA could enhance CRC diagnostic performance. While CEA alone showed an AUC of 0.789 (95% CI: 0.686\u0026ndash;0.893; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), combining CEA with the three-lncRNA panel FUS significantly improved the diagnostic accuracy, achieving the highest AUC of 0.950 (95% CI: 0.898-1.000; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and f).\u003c/p\u003e \u003cp\u003eFor detecting all lesions versus normal controls, CEA alone showed an AUC of 0.723 (95% CI: 0.626\u0026ndash;0.821; p\u0026thinsp;=\u0026thinsp;0.001). The combination of CEA with various three-lncRNA panels significantly improved diagnostic accuracy, with FU\u0026thinsp;+\u0026thinsp;CEA achieving the highest AUC of 0.873 (95% CI: 0.795\u0026ndash;0.952; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while FUS\u0026thinsp;+\u0026thinsp;CEA and US\u0026thinsp;+\u0026thinsp;CEA both reached AUCs of 0.870 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eFor distinguishing advanced lesions from non-advanced adenoma, CEA alone demonstrated limited value with an AUC of 0.669 (95% CI: 0.575\u0026ndash;0.762; p\u0026thinsp;=\u0026thinsp;0.002). Integration of CEA with lncRNA panels markedly enhanced diagnostic performance, with US\u0026thinsp;+\u0026thinsp;CEA showing the best performance (AUC\u0026thinsp;=\u0026thinsp;0.780, 95% CI: 0.699\u0026ndash;0.860; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed closely by CUS\u0026thinsp;+\u0026thinsp;CEA and CRNDE+SNHG17\u0026thinsp;+\u0026thinsp;CEA (AUCs\u0026thinsp;=\u0026thinsp;0.778 and 0.776, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation with Clinicopathological Features\u003c/h2\u003e \u003cp\u003eThe relationships between the expression levels of the four candidate lncRNAs and the clinicopathological features of CRC patients were analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No significant associations were observed between FOXP4-AS1, UCA1, or SNHG17 expression and age, gender, BMI, CEA level, tumor differentiation, AJCC stage, or tumor location.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between clinicopathological characteristics of lncRNAs for colorectal cancer patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFOXP4-AS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCRNDE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUCA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSNHG17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.494(6.240\u0026ndash;8.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.183(2.082\u0026ndash;4.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.406(3.242\u0026ndash;6.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.735(4.336\u0026ndash;7.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.156(5.526\u0026ndash;8.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.790(2.645\u0026ndash;7.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.248(3-284-6.421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.481(3.613\u0026ndash;7.286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGerder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.061(6.602\u0026ndash;8.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.609(2.432\u0026ndash;6.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.251(3-369-6.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.575(5.236\u0026ndash;7.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.185(5-170-8.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.328(1.394\u0026ndash;6.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.5.837(3.228\u0026ndash;6.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.406(2.685\u0026ndash;8.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.161(4.997\u0026ndash;8.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.024(1.301\u0026ndash;5.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.732(2.331\u0026ndash;6.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.389(3.605\u0026ndash;7.637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.919(7.042\u0026ndash;8.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.935(3.233\u0026ndash;7.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.756(5.149\u0026ndash;6.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.374(5.034\u0026ndash;7.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.142(5.310\u0026ndash;8.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.869(1.539\u0026ndash;7.432)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.089(2.498\u0026ndash;5.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.111(2.595\u0026ndash;7.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≧\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.703(6.356-8.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.353(2.466\u0026ndash;6.297)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.797(3.627\u0026ndash;6.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.509(5.127\u0026ndash;7.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.185(6.300-7.732)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.338(0.373\u0026ndash;8.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.415(4.077\u0026ndash;8.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.812(4.045\u0026ndash;10.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate+Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.548(5.732\u0026ndash;8.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.377(2.426\u0026ndash;6.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.251(3.228\u0026ndash;6.378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.374(3.620\u0026ndash;7.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.100(5-451-8.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.377(1.685\u0026ndash;6.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.732(2.253\u0026ndash;6.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.034(3.096\u0026ndash;7.534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.601(6.161\u0026ndash;8.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.609(2.426\u0026ndash;7.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.741(4.033\u0026ndash;7.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.442(5.406\u0026ndash;8.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.212(5.732\u0026ndash;8.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.257(1.976\u0026ndash;7.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.251(3.247\u0026ndash;6.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.442(3.573\u0026ndash;8.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.774(5.492\u0026ndash;8.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.774(5.492\u0026ndash;8.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.310(3.569\u0026ndash;6.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.336(4.245\u0026ndash;7.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eIQR: Interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, Higher CRNDE levels were associated with BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 (p\u0026thinsp;=\u0026thinsp;0.034) and right-sided colon tumors (p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we identified and validated several lncRNAs\u0026mdash;FOXP4-AS1, CRNDE, UCA1, and SNHG17\u0026mdash;as promising diagnostic biomarkers for CRC and its precursors. Through integrated bioinformatics analysis, qRT-PCR validation, and ROC curve analysis, we demonstrated that these lncRNAs exhibited significant dysregulation in plasma samples from patients with adenoma and CRC. The diagnostic performance of these lncRNAs, especially when combined with CEA, highlights their potential for early, non-invasive CRC detection. Unlike most prior blood-based CRC biomarker studies that focused solely on CRC diagnosis, our study specifically included plasma samples from patients with precancerous lesions\u0026mdash;both NAA and AA\u0026mdash;allowing us to explore lncRNAs as potential biomarkers for identifying adenomas with high malignant transformation risk. This addresses a critical gap in CRC screening: the early, non-invasive identification of patients who are likely to progress from benign adenomas to malignant disease.\u003c/p\u003e \u003cp\u003eThe stage-specific dysregulation patterns of these lncRNAs offer insights into the molecular progression of CRC. UCA1 and SNHG17 were upregulated from the NAA stage, suggesting their potential as early indicators of adenoma development. In contrast, CRNDE was selectively elevated in CRC, supporting its role as a specific marker for malignant transformation. FOXP4-AS1 showed significant upregulation in both NAA and CRC, implicating it in both early tumorigenesis and advanced disease progression. Collectively, these findings position these lncRNAs not only as diagnostic markers but also as potential tools for monitoring disease progression and stratifying patient risk.\u003c/p\u003e \u003cp\u003eMechanistically, these lncRNAs have been previously implicated in tumor biology[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. FOXP4-AS1 promotes oncogenesis by acting as a competing endogenous RNA (ceRNA), silencing tumor suppressor genes via epigenetic regulation, and enhancing immune evasion pathways[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. CRNDE has been linked to altered cellular metabolism and immune escape in CRC[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], while UCA1 activates critical signaling pathways such as MAPK/JNK and Wnt/β-catenin[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. SNHG17, a member of the small nucleolar RNA host gene family, contributes to tumor proliferation via suppression of p57 and alternative splicing regulation by RBM10[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The dysregulation of these lncRNAs in our plasma cohort is thus consistent with their established roles in CRC pathogenesis.\u003c/p\u003e \u003cp\u003eOur results delineate the diagnostic utility of these markers across clinically relevant scenarios. For instance, FOXP4-AS1 maintained a moderate but consistent AUC (0.683\u0026ndash;0.707) across all stages versus healthy controls, suggesting its role as a ubiquitous but non-specific marker of early neoplasia. In stark contrast, UCA1 and SNHG17 demonstrated a marked and statistically significant increase from the non-advanced adenoma stage (UCA1: AUC\u0026thinsp;=\u0026thinsp;0.730; SNHG17: AUC\u0026thinsp;=\u0026thinsp;0.723) to the advanced adenoma stage (UCA1: AUC\u0026thinsp;=\u0026thinsp;0.835, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; SNHG17: AUC\u0026thinsp;=\u0026thinsp;0.812, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and reached even higher values in CRC. This graduated enhancement in accuracy parallels the advancing genomic and phenotypic instability along the adenoma-carcinoma sequence, implying that the plasma levels of UCA1 and SNHG17 may reflect increasing tumor burden or specific aggressive biological features. Notably, SNHG17 demonstrated particular value in the clinically challenging task of risk stratification, differentiating non-advanced adenomas from the combined group of advanced lesions (AA and CRC) with an AUC of 0.704. This capability positions SNHG17 not merely as a detection tool, but as a potential triage biomarker to identify, within the adenoma pool, those patients harboring or at immediate risk for high-grade neoplasia, thereby informing personalized surveillance intensity.\u003c/p\u003e \u003cp\u003eAs anticipated, the integration of multiple lncRNAs into combinatorial panels substantially outperformed any single marker. The three-lncRNA panel FUS achieved excellent diagnostic accuracy for CRC (AUC\u0026thinsp;=\u0026thinsp;0.906) and AA (AUC\u0026thinsp;=\u0026thinsp;0.861). More importantly, the differential efficacy of specific combinations for distinct clinical questions highlights the principle of precision biomarker application. The panel CUS, for example, was optimized for distinguishing advanced from non-advanced lesions (AUC\u0026thinsp;=\u0026thinsp;0.716), a task crucial for risk stratification but poorly served by conventional methods. This suggests that tailored biomarker signatures can be developed to answer specific clinical questions, moving beyond a one-size-fits-all diagnostic test.\u003c/p\u003e \u003cp\u003eThe most impactful finding from this study is the remarkable synergy between the novel lncRNA panels and the conventional serum marker CEA. While CEA alone showed limited accuracy for detecting adenomas (AUC\u0026thinsp;=\u0026thinsp;0.695 for AA vs. HC; AUC\u0026thinsp;=\u0026thinsp;0.665 for NAA vs. HC), combining it with the FUS panel greatly enhanced performance, yielding outstanding results (AUC\u0026thinsp;=\u0026thinsp;0.950 for CRC; 0.893 for AA). Notably, the FUS\u0026thinsp;+\u0026thinsp;CEA panel could distinguish any lesion from healthy controls with an AUC of 0.873 makes it a strong candidate for a high-performance, blood-based screening test. This combination not only addresses a significant gap in current screening methods by improving sensitivity for the critical pre-cancerous stage where CEA alone falls short, but it also achieves near-perfect discrimination for CRC.\u003c/p\u003e \u003cp\u003eWe also examined the correlation between lncRNA expression and clinicopathological features in CRC patients. While no significant associations were found between FOXP4-AS1, UCA1, or SNHG17 expression and factors such as age, gender, or tumor differentiation, higher levels of CRNDE were associated with BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 and right-sided colon tumors. This suggests that CRNDE may serve as a useful biomarker for identifying specific subtypes of CRC, such as those with a higher metabolic burden or those located in the right colon. These findings warrant further investigation to explore the potential clinical implications of CRNDE in CRC prognosis.\u003c/p\u003e \u003cp\u003eLiquid biopsy has emerged as a crucial non-invasive tool for early cancer screening due to its convenience and high sensitivity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. By analyzing biomarkers such as circulating tumor DNA (ctDNA), miRNA, lncRNA, and exosomes in plasma, liquid biopsy can provide diagnostic information at the early stages of cancer, especially for detecting CRC and its precursors (such as advanced adenomas), showing immense potential[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For example, in stool-based studies, miR-21 and miR-106a have been identified as significantly overexpressed in individuals with CRC and advanced adenomas, demonstrating the potential of miRNA as a non-invasive biomarker for early detection[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Meanwhile, ctDNA has been widely used in post-surgical monitoring and minimal residual disease detection[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, methylated DNA markers such as SEPT9, BMP3, and NDRG4 have been incorporated into both blood and stool tests to improve sensitivity for advanced adenomas[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the clinical translation of liquid biopsy still faces technical and economic challenges. Firstly, the extraction of biomarkers still requires complex technical support, particularly regarding the purity and cost issues of extracting exosomes and miRNA[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which limits their widespread application in larger populations. Furthermore, although miRNA and lncRNA biomarkers have shown high sensitivity in studies, their consistency and reproducibility across different patient populations still need further validation[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile current research on liquid biopsy biomarkers primarily focuses on early diagnosis and risk stratification, integrating them into clinical practice still requires addressing issues such as biomarker standardization, platform differences, and cost-effectiveness[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, future research should not only focus on large-scale, multi-center validation but also work on improving technology and reducing costs to promote the widespread application of liquid biopsy in early CRC screening.\u003c/p\u003e \u003cp\u003eDespite the promising results, there are still some limitations in this work. First, the sample size was relatively small and all samples were collected from a single center, which may affect the generalizability of the findings. Future studies involving larger, multi-center cohorts are needed to validate these results. Second, the lack of longitudinal data limits our understanding of the prognostic value of these lncRNAs. Long-term follow-up studies are necessary to evaluate their association with disease progression, recurrence, and survival. Additionally, while we identified several lncRNAs with diagnostic potential, their functional roles in CRC remain unclear. Further mechanistic studies are required to elucidate how these lncRNAs influence tumor development and progression. Finally, the cost-effectiveness and practical implementation of lncRNA-based tests in routine clinical settings need to be thoroughly evaluated. While they represent a promising non-invasive alternative, further studies are needed to assess their cost-benefit ratio, scalability, and seamless integration into existing clinical workflows before widespread adoption can be achieved.\u003c/p\u003e \u003cp\u003eIn conclusion, our study identifies FOXP4-AS1, CRNDE, UCA1, and SNHG17 as potential plasma-based biomarkers for the early detection of CRC and adenomas. These lncRNAs, particularly when combined into multi-lncRNA panels, offer a promising, non-invasive approach for CRC screening and diagnosis. Their integration with conventional biomarkers such as CEA could further improve diagnostic accuracy, making these panels an attractive alternative for non-invasive, early detection of colorectal cancer in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong non-coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-advanced adenoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdvanced adenoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative real-time polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarcinoembryonic antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFOXP4-AS1\u0026thinsp;+\u0026thinsp;UCA1\u0026thinsp;+\u0026thinsp;SNHG17\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCRNDE\u0026thinsp;+\u0026thinsp;UCA1\u0026thinsp;+\u0026thinsp;SNHG17\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUCA1\u0026thinsp;+\u0026thinsp;SNHG17\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFOXP4-AS1\u0026thinsp;+\u0026thinsp;UCA1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFOXP4-AS1\u0026thinsp;+\u0026thinsp;SNHG17\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAJCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eceRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCompeting endogenous RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ectDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCirculating tumor DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eWritten informed consent was obtained from each individual participant, and the experimental protocol was approved by the West China Hospital Institutional Review Board (NO.2021\u0026thinsp;\u0026minus;\u0026thinsp;179). Also all experiments were performed in accordance with relevant guidelines and regulations of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work as supported by the Natural Science Foundation of Sichuan Science and Technology Department (2025ZNSFSC1897 and 2025ZNSFSC1899).\u003c/p\u003e \u003cp\u003eAcknowledgements: None.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXB and QL made substantial contributions to the conception and design of the work, material preparation, data collection, and analysis. They wrote the first draft of the manuscript. LM and KD contributed to data collection and analysis. XL and JY supervised the work, contributed to the interpretation of data, and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw RNA-seq data generated during the current study are available in the NCBI Sequence Read Archive (SRA) under BioProject accession number PRJNA690126. Publicly available datasets analysed during the current study include GSE164541 and GSE109203.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Miller, K. D., Wagle, N. S. \u0026amp; Jemal, A. Cancer statistics, 2023. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e ;73:17-48.10.3322/caac.21763 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDekker, E., Tanis, P. J., Vleugels, J. L. A., Kasi, P. M. \u0026amp; Wallace, M. B. 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Role of gut microbiota in epigenetic regulation of colorectal Cancer. \u003cem\u003eBiochim Biophys Acta Rev Cancer\u003c/em\u003e ;1875:188490.10.1016/j.bbcan.2020.188490 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal Cancer (CRC), Long Non-Coding RNA (lncRNA), Liquid Biopsy, Diagnostic Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9169352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to identify and validate long non-coding RNAs (lncRNAs) as diagnostic biomarkers for Colorectal cancer (CRC) and its precursors, focusing on early-stage risk stratification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed bioinformatics analysis of public datasets and in-house RNA-sequencing data to identify dysregulated lncRNAs in the adenoma\u0026ndash;carcinoma sequence. Plasma samples were collected from patients with non-advanced adenomas, advanced adenomas, CRC, and healthy controls. Candidate lncRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis, and lncRNA combinations were assessed to improve accuracy. Integration with CEA was also explored.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 37 differentially expressed lncRNAs, of which four\u0026mdash;FOXP4-AS1, CRNDE, UCA1, and SNHG17\u0026mdash;showed significant dysregulation in plasma. ROC analysis indicated that UCA1 and SNHG17 effectively distinguished advanced adenomas from healthy controls (area under the curve\u0026thinsp;=\u0026thinsp;0.835 and 0.812, respectively). The three-lncRNA panel FOXP4-AS1\u0026thinsp;+\u0026thinsp;UCA1\u0026thinsp;+\u0026thinsp;SNHG17 achieved an area under the curve of 0.906 for CRC detection and 0.861 for advanced adenomas. Combining lncRNAs with CEA further enhanced diagnostic performance, reaching area under the curve values of 0.950 for CRC and 0.893 for advanced adenomas.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFOXP4-AS1, CRNDE, UCA1, and SNHG17 are promising plasma-based biomarkers for CRC and adenoma detection. When combined into multi-lncRNA panels, they offer a non-invasive, high-performance approach for early CRC screening and risk-based stratification. Further large-scale, multi-center validation is needed to establish their clinical applicability and integration into routine screening.\u003c/p\u003e","manuscriptTitle":"Circulating lncRNAs as Early Diagnostic Biomarkers for Colorectal Cancer: A case-control diagnostic accuracy study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:13:22","doi":"10.21203/rs.3.rs-9169352/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-30T13:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T13:28:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T09:35:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T12:19:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-25T11:59:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c7a7b47-2b52-464a-a01f-85de1fdd2e06","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65401133,"name":"Health sciences/Biomarkers"},{"id":65401134,"name":"Biological sciences/Cancer"},{"id":65401135,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65401136,"name":"Health sciences/Gastroenterology"},{"id":65401137,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-02T07:13:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:13:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169352","identity":"rs-9169352","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}