Dysregulation of LncRNA RBM5-AS1, LncRNA VPS9D1-AS1, LncRNA STEAP3-AS1, and wnt/β-catenin provides insights into colorectal cancer diagnosis

Dysregulation of LncRNA RBM5-AS1, LncRNA VPS9D1-AS1, LncRNA STEAP3-AS1, and wnt/β-catenin provides insights into colorectal cancer diagnosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dysregulation of LncRNA RBM5-AS1, LncRNA VPS9D1-AS1, LncRNA STEAP3-AS1, and wnt/β-catenin provides insights into colorectal cancer diagnosis Mohammadreza Karbalaee Hashemiyan, Reza Manouchehri-Ardakani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6534629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Molecular Biology Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Background Colorectal cancer (CRC) continues to be a major contributor to cancer-associated deaths worldwide, largely due to late-stage diagnoses. This study aims to investigate the tissue and plasma expression levels of antisense long non-coding RNAs (lncRNAs) in CRC patients using receiver operating characteristic (ROC) analysis to evaluate their diagnostic potential. Materials and methods The current case-control study included an equal number of plasma (n = 40) and tissue (n = 10) samples from CRC patients and normal controls. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to assess the expression levels of lncRNAs RBM5-AS1, STEAP3-AS1, and VPS9D1-AS1. Additionally, the diagnostic performance of these lncRNAs was evaluated through ROC curve analysis, which also helped to identify the appropriate cutoff values. Results The expression of the β-catenin gene was significantly higher in both CRC tissues (6.78-fold increase, p = 0.02) and plasma samples (4.79-fold increase, p < 0.001) compared to the control group. Similarly, the expression of the lncRNAs was significantly higher in both CRC tissues and plasma samples compared to healthy subjects (p < 0.001). Furthermore, ROC curve analysis demonstrated that these lncRNAs had strong predictive power, with AUC values of 0.82 for STEAP3-AS1, 0.94 for VPS9D1-AS1, and 0.83 for RBM5-AS1. Conclusion As the results showed, the expression levels of β-catenin and lncRNAs STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 in both tissues and plasma samples from CRC patients were higher than those in healthy subjects; thus, they could serve as powerful biomarkers for CRC diagnosis. However, further studies are required to confirm these results and explore new approaches. LncRNAs colorectal cancer biomarker wnt/β-catenin signaling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Colorectal cancer (CRC) is recognized as the third most prevalent malignancy in both males and females globally. As one of the most lethal cancers, approximately 1.8 million new cases of CRC were diagnosed, and over 900,000 patients died from CRC in 2020. The 5-year survival rate in metastatic cases is approximately 10–15% [ 1 ]. Cancer grading and staging are critical components in the diagnosis and management of cancer. Grading refers to the assessment of tumor cells' appearance compared to normal cells, and it indicates how aggressive the cancer is [ 2 ]. On the other hand, staging describes the extent of cancer in the body using the TNM system (Tumor, Node, Metastasis). It classifies tumors based on their size, lymph node involvement, and the presence of metastasis [ 3 ]. Likewise, 60 percent of patients suffering from CRC are diagnosed at advanced stages. Accordingly, developing new diagnostic tests to contribute to the early detection of CRC is crucial [ 4 ]. The tests performed based on body fluids are cost-effective and non-invasive methods for the detection of cancer in the early stages. They provide further essential information for tracking tumor development and evaluating prognosis [ 5 ]. Long non-coding RNAs (lncRNAs) represent a class of transcripts with limited protein-coding potential. Each one has a minimum length of 200 nucleotides [ 6 ]. It has been demonstrated that lncRNAs can regulate several biological pathways, including the Wnt/β-catenin pathway, which is involved in the regulation of cell growth, differentiation, and division [ 7 ]. LncRNAs regulate gene expression by interacting with chromatin and influencing transcriptional activity [ 8 ]. Proven evidence suggests that dysregulation of the Wnt/β-catenin signaling pathway leads to cancer by promoting uncontrolled cell proliferation, survival, and metastasis. Mutations in pathway proteins such as APC, β-catenin, and Axin result in the accumulation of β-catenin, which is then translocated to the nucleus, where it activates the expression of oncogenic genes. Additionally, the overexpression of Wnt ligands or receptors, along with the loss of Wnt pathway inhibitors such as DKK1 and SFRPs, amplifies this signaling and thereby exacerbates cancerous behavior [ 9 ]. It has been reported that the lncRNAs RBM5-AS1 (RBM5 antisense RNA 1), STEAP3-AS1 (STEAP3 antisense RNA 1), and VPS9D1-AS1 (VPS9D1 antisense RNA 1) are dysregulated in various tumors. For instance, RBM5-AS1 exerts pro-oncogenic effects in osteosarcoma, hepatocellular carcinoma, and oral squamous cell carcinoma [ 10 , 11 ]. Moreover, the interaction between lncRNA STEAP3-AS1 and YTH domain-containing family protein 2 (YTHDF2) enhances the stability of STEAP3 mRNA, leading to increased STEAP3 protein expression. This upregulation activates the Wnt/β-catenin signaling pathway in an iron-dependent manner, thereby promoting cancer progression [ 12 ]. Conversely, silencing VPS9D1-AS1 downregulates the Wnt/β-catenin signaling pathway by targeting key proteins such as β-catenin and c-MYC [ 13 ]. However, the biological roles of these lncRNAs in CRC remain poorly understood and have not been extensively studied. The present study aimed to assess the expression levels of the lncRNAs RBM5-AS1, STEAP3-AS1, and VPS9D1-AS1 in CRC tissues and plasma, as well as their interaction with the Wnt/β-catenin signaling pathway. Moreover, the diagnostic potential of these lncRNAs was also evaluated. 2. Materials and Methods 2.1. Materials Plasma samples were collected using VACUETTE K3EDTA-containing tubes (Greiner Bio-One, USA). Total RNA was extracted using TRIzol reagent (Invitrogen, Thermo Fisher Scientific, Massachusetts, USA; Cat. No. 15596026). Complementary DNA (cDNA) synthesis was performed with a cDNA synthesis kit (ADDBIO, South Korea; Cat. No. 22701). Real-time polymerase chain reaction (RT-PCR) amplification was carried out using primers (Pishgam Biotech Company, Iran) and a SYBR Green master mix kit (PARSTOUS, Mashhad, Iran) on an Applied Biosystems system (Thermo Fisher Scientific, Massachusetts, USA). 2.2. Subjects’ recruitment The study population consisted of 40 plasma samples and 10 tissue specimens obtained from patients with colorectal cancer (CRC). Additionally, 40 normal plasma samples and 10 normal colorectal tissue samples were collected from healthy individuals to serve as the control group. All samples were obtained at Beheshti Hospital, Kashan University of Medical Sciences, Kashan, Iran, by a college-affiliated gastroenterologist (Ethical code: IR.KAUMS.MEDNT.REC.1402.259). Before participating in the study, all individuals were thoroughly informed and provided written informed consent. The study was conducted between January 2023 and March 2025. Inclusion criteria were: histological confirmation of colorectal adenocarcinoma, absence of cancer or underlying diseases in healthy individuals providing blood and tissue samples, age ≥ 18 years, and written informed consent. Exclusion criteria included: presence of other cancer types, inflammatory bowel disease, severe comorbidities, pregnancy, or refusal to provide informed consent [ 14 – 16 ]. 2.3. RNA extraction and cDNA synthesis For the extraction of total RNA, TRIzol reagent was added to both tissue and plasma samples, followed by vigorous shaking. Subsequently, chloroform was added. After centrifugation at 15,000 rpm for 10 minutes at 4°C, the mixture separated into two phases, and RNA was isolated from the aqueous phase. To assess the integrity and quantity of the extracted RNA, gel electrophoresis and a NanoDrop spectrophotometer (Thermo Fisher Scientific, One/C) were used, respectively. Complementary DNA (cDNA) synthesis was performed using reverse transcriptase enzyme and random hexamer primers. 2.4. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) RT-qPCR was utilized to assess the tissue and plasma expression levels of the β-catenin gene, lncRNA RBM5-AS1, lncRNA STEAP3-AS1, and lncRNA VPS9D1-AS1 in CRC patients and healthy individuals. The final volume of each qRT-PCR reaction was 20 µl, which included the master mix, cDNA, forward and reverse primers (Table 1 ), and ddH2O, and was performed in triplicate. The thermal cycling protocol included an initial denaturation step at 95°C for 10 minutes, followed by 45 cycles consisting of denaturation at 94°C for 10 seconds, annealing at 63°C for 30 seconds, and extension at 70°C for 20 seconds. qRT-PCR data were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as the internal reference gene. The relative expression levels of the target genes were quantified using the 2 −ΔΔCt method. Table 1 Primer sequences and their product length Primer name Sequences Product length (bp) LncRNA RBM5-AS1 F: AACAAGCCGCCTGAACTAAA 242 R: CTGATTCCCCCAGTCTTCAA LncRNA VPS9D1-AS1 F: GGAAATGTGACAAGCTGCTG 189 R: CGCCGTGTATACTCCGATG LncRNA STEAP3-AS1 F: CACTGCCTCCTCTTGGAAAG 241 R: GCCAGAAGAGATGGACAAGC CTNNB1 (β-catenin) F: CCTGTTCCCCTGAGGGTATT 87 R: CCATCAAATCAGCTTGAGTAGCC GAPDH F: CATGTTGCAACCGGGAAGGA 190 R: GCCCAATACGACCAAATCAGAG 2.5. Histopathological examination Surgical biopsy specimens were collected from ten patients diagnosed with colorectal cancer and ten healthy individuals who had undergone colonoscopy. Approximately 0.5 g of each sample was taken, rinsed with physiological saline solution, and then frozen at -80°C for histological examination. Hematoxylin and eosin (H&E) staining was applied to ensure accurate classification, and the diagnosis of colorectal carcinoma was subsequently confirmed by an expert pathologist. 2.6. Statistical analysis Statistical analysis was performed using SPSS (version 16) and GraphPad Prism (version 10). Several statistical tests were applied, including the Kolmogorov-Smirnov test to assess data normality, Chi-square and independent sample t-test to evaluate differences between two groups, Spearman's test to determine the statistical relationship among different variables, and ROC curve analysis to identify the optimal cutoff values for each diagnostic parameter. In this study, a p-value of < 0.05 was considered statistically significant, and all data are presented as mean ± standard error of the mean (SEM). 3. Result 3.1. Patients and samples Forty plasma specimens and ten colon tissue samples were collected from both CRC patients and healthy controls. Demographic information was gathered during the sampling process. There were no significant differences between the groups regarding age, gender, or smoking status (Table 2 ). Table 2 Demographic data of the patients with CRC and healthy individuals Characteristics Plasma Tissue CRCs (n = 40) Controls (n = 40) p-values* CRC (n = 10) Controls (n = 10) p-values Gender Male n (%) 23(57.5) 25(62.5) 0.82 6(60) 6(60) > 0.99 Female n (%) 17(42.5) 15(37.5) 4(40) 4(40) Age (years) 30, 65 23(57.5) 14(35) 6(60) 4(40) Smoking n (%) Y 20(50) 17(42.5) 0.65 6(60) 3(30) 0.36 N 20(50) 23(57.5) 4(40) 7(70) Weight n (%) 0.99 > 70 24(60) 20(50) 6(60) 5(50) Degree of differentiation High 8(20) - 0(0) - Moderate 14(35) - 6(60) - Poor 18(45) - 4(40) - Stage I 4(10) - 1(10) - II 24(60) - 6(60) - III 7(17.5) - 2(20) - IV 5(12.5) - 1(10) - *p-value was reported based on chi-square test; weights are based on kilogram. 3.2. Tissue samples A blinded evaluation by an expert pathologist confirmed that all tissues from the CRC group displayed definitive malignant characteristics, including glandular disorganization, nuclear pleomorphism, and elevated mitotic activity, consistent with cancerous pathology (Fig. 1 A). In contrast, tissues from the control group displayed normal histological architecture, with no evidence of neoplastic transformation (Fig. 1 B). 3.3. Relative expression levels of β-catenin mRNA in CRC and control group. The mRNA expression levels of β-catenin in CRC tissues were significantly higher than those in the control group (7-fold increase; p = 0.02) (Fig. 2 A). Furthermore, plasma β-catenin mRNA expression levels were also significantly higher in CRC patients, showing nearly a 5-fold increase compared to normal plasma samples (p < 0.001) (Fig. 2 B). 3.4 Tissue‑specific expression of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1 in CRC compared to the control group. Tissue expression levels of lncRNA STEAP3-AS1 were significantly higher in the patient group compared to the control group (5-fold increase; p = 0.023) (Fig. 3 A). Similarly, the expression of lncRNA VPS9D1-AS1 was markedly higher in CRC patients compared to healthy controls, with an approximately 4-fold increase (p < 0.001) (Fig. 3 B). Furthermore, the expression of lncRNA RBM5-AS1 was also significantly higher in CRC patients compared to control subjects, with a 6-fold increase (p = 0.004) (Fig. 3 C). 3.5 Plasmaspecific Expression of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1 in CRC compared to the control group. Plasma expression levels of lncRNA STEAP3-AS1 were significantly higher, with approximately a 6-fold increase compared to the control group (p < 0.001) (Fig. 4 A). Similarly, lncRNA VPS9D1-AS1 showed significantly higher expression relative to the control group, with a 5-fold increase (p < 0.001) (Fig. 4 B). Additionally, the expression of lncRNA RBM5-AS1 was significantly elevated in CRC patients, with a 5-fold increase compared to the control group (p < 0.001) (Fig. 4 C). 3.6 Comparison of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA expression between tissue and plasma in the CRC group. Analysis of lncRNA STEAP3-AS1 expression levels showed no significant differences between plasma and tissue samples in the CRC group. qRT-PCR results indicated that the relative expression levels of lncRNA STEAP3-AS1 in plasma were comparable to those in tissue samples (6.15 ± 0.5 vs 5.03 ± 0.9; p = 0.26) (Fig. 5 A). Moreover, the expression levels of lncRNA VPS9D1-AS1 showed no significant differences between plasma and tissue samples in the CRC group (5.03 ± 0.21 vs 4.23 ± 0.5; p = 0.08) (Fig. 5 B). Likewise, lncRNA RBM5-AS1 expression showed no significant variation between plasma and tissue samples in the CRC group (5.3 ± 0.22 vs 5.89 ± 1.09; p = 0.61) (Fig. 5 C). Finally, expression analysis of β-catenin mRNA revealed no statistically significant differences between plasma and tissue samples (4.79 ± 0.38 vs 6.78 ± 1.18; p = 0.07) (Fig. 5 D). 3.7. Subgroup Evaluation of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA in CRC Patients The expression levels of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA were investigated in relation to pathological stages, differentiation, and other clinical parameters. No significant associations were found between the expression levels of these lncRNAs and the clinical parameters (Table 2 ). 3.7. ROC curve analysis ROC curve analysis was used to evaluate the diagnostic value of plasma lncRNAs in CRC patients. The predictive value of plasma β-catenin levels was determined with an area under the curve (AUC) of 0.88 (95% confidence interval (CI) [ 17 ] 0.79–0.94; p < 0.001), with a sensitivity of 92.5% and specificity of 90.0% (Fig. 6 A). Additionally, the plasma expression level of lncRNA STEAP3-AS1 demonstrated an AUC of 0.82 (95% CI: 0.72–0.89, p < 0.001), with a sensitivity of 70% and specificity of 90% (Fig. 6 B). Notably, the plasma expression level of lncRNA VPS9D1-AS1 exhibited impressive diagnostic performance with an AUC of 0.94 (95% CI: 0.87–0.98, p < 0.001), alongside a sensitivity and specificity of 92.5% (Fig. 6 C). Moreover, the AUC related to lncRNA RBM5-AS1 was 0.83 (95% CI: 0.73–0.91, p < 0.001), with a sensitivity of 90% and specificity of 77.14% (Fig. 6 D). Finally, combining the plasma expression levels of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA further improved the AUC to 0.97 (95% CI: 0.93–0.99, p < 0.001) with 99% sensitivity and 58% specificity (Fig. 6 E) (Table 3 ). Table 3 Subgroup analysis of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1, and β-catenin mRNA in CRC. Clinicopathological factor n β-catenin mRNA LncRNA STEAP3-AS1 LncRNA VPS9D1-AS1 LncRNA RBM5-AS1 Changing fold P-Value* Changing fold P-Value Changing fold P-Value Changing fold P-Value Age (years) 0.75 0.53 0.68 0.82 30, 65 23 4.88 ± 0.46 6.42 ± 0.65 4.85 ± 0.29 5.26 ± 0.30 Pathological stage 0.54 0.50 0.06 0.31 I 4 5.19 ± 1.24 3.93 ± 1.72 4.53 ± 0.65 5.06 ± 1.35 II 24 4.51 ± 0.55 6.74 ± 0.51 5.16 ± 0.14 5.31 ± 0.22 III 7 5.70 ± 0.79 5.86 ± 1.72 3.84 ± 0.60 4.70 ± 0.63 IV 5 4.53 ± 0.53 5.46 ± 1.86 6.46 ± 0.98 6.26 ± 0.54 Degree of differentiation 0.41 0.11 0.59 0.22 High 8 3.77 ± 0.97 5.20 ± 0.95 4.91 ± 0.34 4.77 ± 0.66 Moderate 14 5.10 ± 0.55 7.61 ± 0.61 4.95 ± 0.25 5.80 ± 0.35 Poor 18 5.00 ± 0.60 5.42 ± 0.88 5.14 ± 0.42 5.14 ± 0.28 Weight 0.37 0.83 0.61 0.19 70 24 5.46 ± 0.74 5.83 ± 0.82 4.78 ± 0.20 5.59 ± 0.39 Gender 0.39 0.49 0.53 0.65 Male 23 5.30 ± 0.59 6.11 ± 0.74 4.97 ± 0.33 5.34 ± 0.28 Female 17 4.09 ± 0.63 6.19 ± 0.69 5.11 ± 0.23 5.24 ± 0.36 *: p-value was reported based on The Mann Whitney test for two groups and Kruskal-Wallis one-way ANOVA for more than two groups; **: measurement data were expressed plasma-specific expression levels as mean ± The standard error of the mean. Table 4 The area under curve and other calculated parameter for ROC analysis AUC (95% CI) Sensitivity (%) (95% CI) Specificity (%) (95% CI) Cut-off value LR+ LR- p-value β-catenin 0.88(0.79–0.94) 92.50 (79.6–98.4) 90.00 (76.3–97.2) > 1.33 9.25 0.08 5.7 7.00 0.33 3.1 12.33 0.08 3.29 3.94 0.13 < 0.01 3.8. Correlation analysis of clinical and gene expression variables We performed correlation coefficient analysis to assess the strength and direction of the linear relationships between variables [ 18 ]. There were moderate positive correlations between age with degree of differentiation (r = 0.20, p = 0.59; Fig. 7 A), pathological stage (r = 0.22, p = 0.18; Fig. 7 A), β-catenin expression (r = 0.12, p = 0.47; Fig. 7 B), and lncRNA STEAP3-AS1 expression (r = 0.18, p = 0.25; Fig. 7 C). Conversely, there were weak to moderate inverse correlations with lncRNA VPS9D1-AS1 expression (r = -0.19, p = 0.23; Fig. 7 D) and lncRNA RBM5-AS1 expression (r = -0.15, p = 0.34; Fig. 7 E). Patients' weight showed weak positive correlations with β-catenin expression (r = 0.14, p = 0.38; Fig. 7 F), lncRNA STEAP3-AS1 expression (r = 0.01, p = 0.96; Fig. 7 G), lncRNA VPS9D1-AS1 expression (r = 0.01, p = 0.91; Fig. 7 H), and lncRNA RBM5-AS1 expression (r = 0.26, p = 0.09; Fig. 7 I). Additionally, body weight had significant positive correlations with both the degree of differentiation (r = 0.37, p = 0.019) and pathological stage (r = 0.30, p = 0.008). No significant correlations were observed between the expression levels of each pair of studied lncRNAs; however, lncRNA RBM5-AS1 exhibited a slight positive correlation with lncRNA VPS9D1-AS1 (r = 0.12, p = 0.29) and lncRNA STEAP3-AS1 (r = 0.10, p = 0.33). Similarly, lncRNA STEAP3-AS1 showed a slight positive correlation with lncRNA VPS9D1-AS1 (r = 0.20, p = 0.80). 4. Discussion Despite significant advances in detection and therapeutic strategies, CRC remains one of the leading causes of cancer-related mortality. This high mortality is partly due to the absence of effective early diagnostic modalities and the emergence of chemoresistance [ 19 ]. Recent technological advancements have facilitated the identification of numerous dysregulated signaling pathways that contribute to the development of CRC [ 20 ] Additionally, some researchers have reported that aberrations in the Wnt/β-catenin signaling cascade have emerged as a critical contributor to oncogenesis, which promotes cancer development by promoting angiogenesis, metastasis, and drug resistance [ 20 – 23 ]. β-catenin, a central effector in the Wnt/β-catenin signaling pathways, is frequently dysregulated in CRC. Its overactivation results in the activation of growth-promoting genes, leading to uncontrolled cell proliferation [ 24 , 25 ]. Our study specifically investigated the expression of β-catenin mRNA in CRC tissues and plasma, comparing it to a control group, in order to assess the role of β-catenin mRNA and the dysregulation of the Wnt/β-catenin signaling pathway in CRC onset, progression, and metastasis. Consistent with previous reports, our results revealed significantly higher β-catenin mRNA expression in CRC tissues compared to the control group. Furthermore, plasma-specific expression of β-catenin mRNA was also elevated. The strong interaction between lncRNAs and the Wnt/β-catenin pathway is well-documented [ 26 ]. The present study focused on the levels of three lncRNAs—STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1—and their relationship with β-catenin mRNA expression, aiming to evaluate their potential as diagnostic biomarkers in CRC patients. The results showed that the tissue expression levels of STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 were significantly higher in CRC patients compared to the control group. These findings are supported by existing evidence; under hypoxic conditions, a common feature of the solid tumor microenvironment, HIF-1α becomes stabilized and transcriptionally induces the expression of lncRNA STEAP3-AS1 [ 27 ]. Mechanistically, lncRNA STEAP3-AS1 exerts its oncogenic effects by competitively binding to the N6-methyladenosine (m6A) 'reader' protein YTHDF2, which typically targets m6A-modified mRNAs for degradation [ 12 ]. Sequestration of YTHDF2 by lncRNA STEAP3-AS1 leads to stabilization of STEAP3 mRNA, which, in turn, increases the translation of STEAP3 protein. As a metalloreductase, STEAP3 catalyzes the conversion of ferric iron (Fe³⁺) to ferrous iron (Fe²⁺), resulting in the phosphorylation and subsequent inactivation of glycogen synthase kinase-3β (GSK3β) at serine-9 [ 28 ]. Inactivation of GSK3β prevents the degradation of β-catenin, allowing its accumulation and nuclear translocation, where it activates transcriptional programs that promote cell proliferation, migration, and invasion [ 29 ]. Similarly, in esophageal squamous cell carcinoma (ESCC), lncRNA VPS9D1‐AS1 is significantly upregulated in tumor tissues [ 30 ]. Functional studies in ESCC cell lines have shown that silencing lncRNA VPS9D1‐AS1 inhibits cell proliferation, migration, invasion, and colony formation [ 13 ]. Mechanistically, lncRNA VPS9D1‐AS1 appears to regulate wnt/β‐catenin signaling, as its knockdown results in decreased levels of β‐catenin and its downstream targets (e.g., c-Myc). Furthermore, the depletion of lncRNA VPS9D1‐AS1 induces G0/G1 cell cycle arrest and downregulates key cell cycle regulators (CDK4, CDK6, Cyclin D1), highlighting its role in promoting cell proliferation [ 31 ]. The lncRNA RBM5-AS1 (also referred to as LUST) has primarily been studied in breast cancer patients, where it is significantly upregulated compared to normal tissue [ 32 ]. the nucleus by suppressing AXIN1, a key component of the β‐catenin destruction complex, potentially through the recruitment of transcriptional regulators such as CTCF. This stabilization supports the assembly of the β‐catenin–TCF4 transcriptional complex, leading to the increased expression of wnt target genes (e.g., c-Myc and Cyclin D1). Furthermore, hypoxic conditions may induce the expression of lncRNA RBM5‐AS1 via transcription factors such as RUNX2, thereby linking tumor hypoxia to Wnt pathway activation [ 32 ]. Despite existing approval documents regarding the higher expression of lncRNAs STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 in CRC tissues compared to normal subjects, there has been limited investigation to confirm this in matched plasma samples for potential use as biomarkers. The present study, therefore, focused on measuring the plasma levels of these lncRNAs to explore their potential for early detection and monitoring of disease progression. Plasma analysis in CRC patients revealed significantly higher expression of these three lncRNAs compared to the control group. To validate these observations, we compared the tissue and plasma expression levels for each lncRNA. The results suggest a strong correlation between tissue and plasma expression. ROC curve analysis was conducted to assess the diagnostic potential of lncRNAs STEAP3-AS1, VPS9D1-AS1, RBM5-AS1, and the β-catenin gene individually, as well as in combination, based on AUC values. The results demonstrated that the diagnostic performance of these lncRNAs and the β-catenin gene in identifying CRC patients was both acceptable and accurate. Specifically, lncRNA VPS9D1-AS1 and the β-catenin gene exhibited the highest sensitivity, while lncRNA STEAP3-AS1 showed the lowest sensitivity. Regarding specificity, lncRNA VPS9D1-AS1 had the highest value, while lncRNA RBM5-AS1 had the lowest. Notably, lncRNA VPS9D1-AS1 outperformed the other markers in discriminating CRC patients from the control group, based on its AUC data. Additionally, the combination of all three lncRNAs and the β-catenin gene was assessed, and the results indicated that this combination improved the AUC and sensitivity, though it reduced specificity. These findings highlight the potential of STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 as promising diagnostic biomarkers for CRC. To examine potential associations between β-catenin and the expression of lncRNAs (STEAP3-AS1, VPS9D1-AS1, RBM5-AS1) with various clinical parameters, including age and weight, Spearman’s correlation analysis was conducted. The analysis revealed no significant associations between lncRNA expression and key clinicopathological factors such as tumor differentiation, stage, or patient weight. Similarly, no significant correlations were found with age. However, a significant correlation was observed between tumor differentiation and stage, as well as between body weight and both of these parameters. This association may be attributed to the well-established link between obesity and CRC. Previous studies have demonstrated that obesity is strongly associated with an increased risk of CRC, primarily due to its role in chronic inflammation and metabolic dysregulation. Conclusion The results demonstrated that the expression levels of tissue and plasma lncRNAs (STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1) in CRC patients were significantly higher than those in healthy subjects. Furthermore, ROC curve analysis confirmed the diagnostic potential of these lncRNAs as well as β-catenin as biomarkers for CRC. Correlation analysis, however, showed no significant associations between the expression of these lncRNAs and patient age or weight. Nevertheless, the study has some limitations, including a relatively small sample size, reliance on a single analytical method to measure each laboratory parameter, and the absence of patient outcome data. To further validate the prognostic and diagnostic significance of these biomarkers, larger-scale studies are necessary Declarations Funding This work was supported by Kashan University of Medical Science, Kashan, Iran (grant numbers 402165, 2023). Acknowledgments The authors gratefully acknowledge the Gastroenterology Department of Kashan Beheshti Hospital, the Research Center of Biochemistry and Nutrition in Metabolic Diseases, and the Anatomical Sciences Research Center at Kashan University of Medical Sciences, Kashan, Iran, for their technical support. The authors also extend their sincere gratitude to all participants involved in this study. Availability of data and materials Data can be accessed upon request. The datasets used in this study are available from the corresponding author upon reasonable request. Consent for publication Informed consent was obtained from all participants in the study. Declaration of competing interest The authors state that they have no conflicts of interest. Author contributions Mohsen Hemmati-Dinarvand conceived of the study, developed the study protocol, participated in the design of the study. Mohammadreza Karbalaee Hashemiyan participated in analyzed the data, review ROC curve chart and wrote the paper. Reza Manouchehri-Ardakani and Mohsen Razavizade contributed in sample collecting, abstracted data and reviewed the references. Ali Rafat reviewed the manuscript and advised on revisions to the manuscript. References Sung H et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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J Hematol Oncol 11(1):113. 10.1186/s13045-018-0656-7 Chen S, Shen X (2020) Long noncoding RNAs: functions and mechanisms in colon cancer. Mol Cancer 19(1):167. 10.1186/s12943-020-01287-2 Liu J et al (2022) Wnt/β-catenin signalling: function, biological mechanisms, and therapeutic opportunities. Signal Transduct Target Ther 7(1):3. 10.1038/s41392-021-00762-6 Deng B et al (2021) LncRNA RBM5-AS1 Promotes Osteosarcoma Cell Proliferation, Migration, and Invasion. Biomed Res Int, 2021: p. 5271291. 10.1155/2021/5271291 Li C et al (2020) Long non-coding RNA RBM5-AS1 promotes the aggressive behaviors of oral squamous cell carcinoma by regulation of miR-1285-3p/YAP1 axis. Biomed Pharmacother 123:109723. 10.1016/j.biopha.2019.109723 Zhou L et al (2022) Hypoxia-induced lncRNA STEAP3-AS1 activates Wnt/β-catenin signaling to promote colorectal cancer progression by preventing m(6)A-mediated degradation of STEAP3 mRNA. Mol Cancer 21(1):168. 10.1186/s12943-022-01638-1 Ma L et al (2021) Long noncoding RNA VPS9D1-AS1 promotes esophageal squamous cell carcinoma progression via the Wnt/β-catenin signaling pathway. J Cancer 12(22):6894–6904. 10.7150/jca.54556 Batra A, Kong S, Cheung WY (2020) Eligibility of Real-World Patients With Stage II and III Colon Cancer for Adjuvant Chemotherapy Trials. Clin Colorectal Cancer 19(4):e226–e234. 10.1016/j.clcc.2020.05.005 Lichtman SM et al (2017) Modernizing Clinical Trial Eligibility Criteria: Recommendations of the American Society of Clinical Oncology–Friends of Cancer Research Organ Dysfunction, Prior or Concurrent Malignancy, and Comorbidities Working Group. J Clin Oncol 35(33):3753–3759. 10.1200/JCO.2017.74.4102 Dasari A et al (2022) NRG-GI008: Colon adjuvant chemotherapy based on evaluation of residual disease (CIRCULATE-US). Journal of Clinical Oncology, 40(4_suppl): p. TPS212-TPS212. 10.1200/JCO.2022.40.4_suppl.TPS212 Aizawa T et al (2019) Cancer-associated fibroblasts secrete Wnt2 to promote cancer progression in colorectal cancer. Cancer Med 8(14):6370–6382. 10.1002/cam4.2523 Mukaka MM (2012) Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24(3):69–71 Weiser MR (2018) AJCC 8th Edition: Colorectal Cancer. Ann Surg Oncol 25(6):1454–1455. 10.1245/s10434-018-6462-1 Martin-Orozco E et al (2019) WNT Signaling in Tumors: The Way to Evade Drugs and Immunity. Front Immunol 10:2854. 10.3389/fimmu.2019.02854 Gore AV et al (2011) Rspo1/Wnt signaling promotes angiogenesis via Vegfc/Vegfr3. Development 138(22):4875–4886. 10.1242/dev.068460 Dahlmann M et al (2016) S100A4 in Cancer Metastasis: Wnt Signaling-Driven Interventions for Metastasis Restriction. Cancers (Basel) 8(6). 10.3390/cancers8060059 Nguyen DX et al (2009) WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell 138(1):51–62. 10.1016/j.cell.2009.04.030 Miao Z, Zhao X, Liu X (2023) Hypoxia induced β-catenin lactylation promotes the cell proliferation and stemness of colorectal cancer through the wnt signaling pathway. Exp Cell Res 422(1):113439. 10.1016/j.yexcr.2022.113439 Zhao H et al (2022) Wnt signaling in colorectal cancer: pathogenic role and therapeutic target. Mol Cancer 21(1):144. 10.1186/s12943-022-01616-7 Wu T et al (2024) Crosstalk between lncRNAs and Wnt/β-catenin signaling pathways in lung cancers: From cancer progression to therapeutic response. Noncoding RNA Res 9(3):667–677. 10.1016/j.ncrna.2024.02.013 Yang L et al (2017) A Gene Signature for Selecting Benefit from Hypoxia Modification of Radiotherapy for High-Risk Bladder Cancer Patients. Clin Cancer Res 23(16):4761–4768. 10.1158/1078-0432.CCR-17-0038 Sendamarai AK et al (2008) Structure of the membrane proximal oxidoreductase domain of human Steap3, the dominant ferrireductase of the erythroid transferrin cycle. Proc Natl Acad Sci U S A 105(21):7410–7415. 10.1073/pnas.0801318105 Wang LL et al (2021) STEAP3 promotes cancer cell proliferation by facilitating nuclear trafficking of EGFR to enhance RAC1-ERK-STAT3 signaling in hepatocellular carcinoma. Cell Death Dis 12(11):1052. 10.1038/s41419-021-04329-9 Huang G et al (2022) VPS9D1-AS1, a novel long-non-coding RNA, acts as a tumor promoter by regulating the miR-324-5p/ITGA2 axis in colon adenocarcinoma. Am J Transl Res 14(2):955–966 Kawasaki Y et al (2016) MYU, a Target lncRNA for Wnt/c-Myc Signaling, Mediates Induction of CDK6 to Promote Cell Cycle Progression. Cell Rep 16(10):2554–2564. 10.1016/j.celrep.2016.08.015 Li X et al (2022) Hypoxia-induced lncRNA RBM5-AS1 promotes tumorigenesis via activating Wnt/β-catenin signaling in breast cancer. Cell Death Dis 13(2):95. 10.1038/s41419-022-04536-y Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jun, 2025 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Revision requested 15 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 02 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 29 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 26 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6534629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451101693,"identity":"acd6473b-6696-426f-8098-9bbf1ba57acf","order_by":0,"name":"Mohammadreza Karbalaee Hashemiyan","email":"","orcid":"","institution":"Center for Biochemistry and Nutrition in Metabolic Diseases, Kashan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"Karbalaee","lastName":"Hashemiyan","suffix":""},{"id":451101694,"identity":"6f6faebe-0d52-4816-9074-5ff22b78bcb5","order_by":1,"name":"Reza Manouchehri-Ardakani","email":"","orcid":"","institution":"Autoimmune Diseases Research Center, Kashan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Manouchehri-Ardakani","suffix":""},{"id":451101695,"identity":"b6dde3dd-7112-40b1-9ae6-eb067d9864d1","order_by":2,"name":"Mohsen Razavizade","email":"","orcid":"","institution":"Gastroenterology Department, Beheshti Hospital, Kashan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Razavizade","suffix":""},{"id":451101696,"identity":"1bc71971-46ac-46ec-8b5c-dcf738019c4f","order_by":3,"name":"Ali Rafat","email":"","orcid":"","institution":"Anatomical Sciences Research Center, Institute for Basic Sciences, Kashan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Rafat","suffix":""},{"id":451101697,"identity":"76ebd968-b503-46a0-8386-446acd8e1d5a","order_by":4,"name":"Mohsen Hemmati-Dinarvand","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACgwM8YDqBsb/5AANjAxFaLGFamGccSyBOiz1MC3tDjgFxWsyO9x7+8HEHQx5vw5lvEj932MgxsB8+ugGvljPn0iRnnmEolmzu3SbZeybNmIEnLe0GXi03csyYedsYEjc2nN0mwdt2OLFBgscMrxaDGznGn/8Ctew/kPNM8i+RWgykGYFaGhty2KSJs+XMGTPJXpCWGceMrWXb0ozZCPnF4HiP8YefIC39zQ9vvm2zkeNnP3wMrxYo+A8iWCRAJBsRyuGA+QMpqkfBKBgFo2DkAAAmdFNdDkJFRQAAAABJRU5ErkJggg==","orcid":"","institution":"Center for Biochemistry and Nutrition in Metabolic Diseases, Kashan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Hemmati-Dinarvand","suffix":""}],"badges":[],"createdAt":"2025-04-26 11:08:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6534629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6534629/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11033-025-10696-9","type":"published","date":"2025-06-16T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82164848,"identity":"ed997986-9a7d-4c4b-9784-059cc3328db9","added_by":"auto","created_at":"2025-05-07 09:07:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eThe image shows colorectal tissue with closely packed crypts, typical of the colon. These crypts are lined by columnar epithelial cells with basal nuclei and apical cytoplasm. The surrounding stroma contains connective tissue, fibroblasts, and immune cells, supporting structural and immune functions. Some crypts have lumens filled with mucus or cellular debris. Colorectal cancer (CRC) tissue displays malignant features, including glandular distortion, nuclear pleomorphism, and increased mitotic activity (A). Normal histological architecture (arrow, without star) shows no neoplastic transformation, while the adjacent tumor area exhibits enlarged, irregular, and mitotically active cells, indicative of malignancy (B). (Hematoxylin and eosin (H\u0026amp;E) staining, scale bars = 100 μm).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/a1aaf805ae6e1c6c92c7ec09.png"},{"id":82168630,"identity":"ce72c4b6-8231-40ca-a886-19ad5526ea67","added_by":"auto","created_at":"2025-05-07 09:31:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165315,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of β-catenin mRNA expression levels in Colorectal tissues (A) and Plasma (B) between the CRC group and the Control group. The results of RT-PCR revealed that the tissue-specific β-catenin mRNA expression levels were significantly higher in the CRC group compared to the control group. Similarly, plasma-specific β-catenin mRNA expression levels were also significantly elevated in the CRC group relative to the control group, data confirmed by the Mann-Whitney U test (*P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/f748bfa31979941642cb4e9c.jpg"},{"id":82166800,"identity":"dde12c40-327a-4c97-9a59-673831306bfb","added_by":"auto","created_at":"2025-05-07 09:15:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245620,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of LncRNA STEAP3-AS1(A), LncRNA VPS9D1-AS1(B) and LncRNA RBM5-AS1(C) expression levels in Tissue samples from CRC and control groups. RT-PCR analysis demonstrated significantly higher expression levels of these markers in the Tissue of the experimental group compared to the control group, the Mann-Whitney U test confirmed significant differences (*P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/742fd7aaa496959894e409a6.jpg"},{"id":82164846,"identity":"0ae42990-3dc1-4cdb-ac4a-81ea8946a598","added_by":"auto","created_at":"2025-05-07 09:07:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":247206,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of plasma expression levels of LncRNA STEAP3-AS1 (A), LncRNA VPS9D1-AS1 (B), and LncRNA RBM5-AS1 (C) in CRC and control groups. RT-PCR results indicated that the expression levels of these LncRNA were significantly upregulated in the plasma of the CRC group compared to the control group, the Mann-Whitney U test were used for confirmation (*P\u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/061800a9e737acdf4ae8bb99.jpg"},{"id":82164901,"identity":"71a62235-7c29-45cb-b2af-631d746cc4b8","added_by":"auto","created_at":"2025-05-07 09:07:48","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":333821,"visible":true,"origin":"","legend":"\u003cp\u003ea graphical representation of gene expression analysis, comparing the expression levels of LncRNA STEAP3-AS1(A), LncRNA VPS9D1-AS1(B), LncRNA RBM5-AS1(C), and β-catenin(D) mRNA between plasma and tissue samples. The data, obtained through RT-qPCR. The analysis revealed no statistically significant differences in the expression of these biomarkers between plasma and tissue samples, as confirmed by the Mann-Whitney U test (P values \u0026gt; 0.05 for all comparisons).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/2c1e24c12f059e02c977938f.jpg"},{"id":82164876,"identity":"85d91c27-7b7d-4efd-8a2d-dbe2cd83c5f5","added_by":"auto","created_at":"2025-05-07 09:07:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":572511,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis demonstrating the diagnostic potential of blood fold changes in (A) β-catenin, (B) LncRNA STEAP3-AS1, (C) LncRNA VPS9D1-AS1, and (D) LncRNA RBM5-AS1. Panel (E) highlights the enhanced performance achieved by combining all four factors into a unified model.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/81044127572a219d490e758f.jpg"},{"id":82166805,"identity":"cdcd9c85-3aea-4d7b-be00-870af77d8a90","added_by":"auto","created_at":"2025-05-07 09:15:46","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":603722,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrates the correlation analysis between various parameters. Panel (A) displays a correlation heatmap, where positive correlations are represented in blue and negative correlations in red. The corresponding R-values for each pair of parameters are indicated within the heatmap. Panels (B) to (E) depict the relationships between age and the expression levels of β-catenin (B), LncRNA STEAP3-AS1 (C), LncRNA VPS9D1-AS1 (D), and LncRNA RBM5-AS1 (E), respectively. Similarly, panels (F) to (I) show the relationships between body weight and the expression levels of β-catenin (F), LncRNA STEAP3-AS1 (G), LncRNA VPS9D1-AS1 (H), and LncRNA RBM5-AS1 (I).\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/efe6a08380df4a8363fe81f1.jpg"},{"id":85231271,"identity":"4b44f2c7-4a3e-4e57-bdb8-0d61f3b590dc","added_by":"auto","created_at":"2025-06-23 16:03:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3419172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6534629/v1/2fb4530d-7ef0-44b4-8a41-a039449bdea0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysregulation of LncRNA RBM5-AS1, LncRNA VPS9D1-AS1, LncRNA STEAP3-AS1, and wnt/β-catenin provides insights into colorectal cancer diagnosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is recognized as the third most prevalent malignancy in both males and females globally. As one of the most lethal cancers, approximately 1.8\u0026nbsp;million new cases of CRC were diagnosed, and over 900,000 patients died from CRC in 2020. The 5-year survival rate in metastatic cases is approximately 10\u0026ndash;15% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cancer grading and staging are critical components in the diagnosis and management of cancer. Grading refers to the assessment of tumor cells' appearance compared to normal cells, and it indicates how aggressive the cancer is [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. On the other hand, staging describes the extent of cancer in the body using the TNM system (Tumor, Node, Metastasis). It classifies tumors based on their size, lymph node involvement, and the presence of metastasis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Likewise, 60 percent of patients suffering from CRC are diagnosed at advanced stages. Accordingly, developing new diagnostic tests to contribute to the early detection of CRC is crucial [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The tests performed based on body fluids are cost-effective and non-invasive methods for the detection of cancer in the early stages. They provide further essential information for tracking tumor development and evaluating prognosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLong non-coding RNAs (lncRNAs) represent a class of transcripts with limited protein-coding potential. Each one has a minimum length of 200 nucleotides [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It has been demonstrated that lncRNAs can regulate several biological pathways, including the Wnt/β-catenin pathway, which is involved in the regulation of cell growth, differentiation, and division [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. LncRNAs regulate gene expression by interacting with chromatin and influencing transcriptional activity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Proven evidence suggests that dysregulation of the Wnt/β-catenin signaling pathway leads to cancer by promoting uncontrolled cell proliferation, survival, and metastasis. Mutations in pathway proteins such as APC, β-catenin, and Axin result in the accumulation of β-catenin, which is then translocated to the nucleus, where it activates the expression of oncogenic genes. Additionally, the overexpression of Wnt ligands or receptors, along with the loss of Wnt pathway inhibitors such as DKK1 and SFRPs, amplifies this signaling and thereby exacerbates cancerous behavior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It has been reported that the lncRNAs RBM5-AS1 (RBM5 antisense RNA 1), STEAP3-AS1 (STEAP3 antisense RNA 1), and VPS9D1-AS1 (VPS9D1 antisense RNA 1) are dysregulated in various tumors. For instance, RBM5-AS1 exerts pro-oncogenic effects in osteosarcoma, hepatocellular carcinoma, and oral squamous cell carcinoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, the interaction between lncRNA STEAP3-AS1 and YTH domain-containing family protein 2 (YTHDF2) enhances the stability of STEAP3 mRNA, leading to increased STEAP3 protein expression. This upregulation activates the Wnt/β-catenin signaling pathway in an iron-dependent manner, thereby promoting cancer progression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, silencing VPS9D1-AS1 downregulates the Wnt/β-catenin signaling pathway by targeting key proteins such as β-catenin and c-MYC [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the biological roles of these lncRNAs in CRC remain poorly understood and have not been extensively studied.\u003c/p\u003e \u003cp\u003eThe present study aimed to assess the expression levels of the lncRNAs RBM5-AS1, STEAP3-AS1, and VPS9D1-AS1 in CRC tissues and plasma, as well as their interaction with the Wnt/β-catenin signaling pathway. Moreover, the diagnostic potential of these lncRNAs was also evaluated.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePlasma samples were collected using VACUETTE K3EDTA-containing tubes (Greiner Bio-One, USA). Total RNA was extracted using TRIzol reagent (Invitrogen, Thermo Fisher Scientific, Massachusetts, USA; Cat. No. 15596026). Complementary DNA (cDNA) synthesis was performed with a cDNA synthesis kit (ADDBIO, South Korea; Cat. No. 22701). Real-time polymerase chain reaction (RT-PCR) amplification was carried out using primers (Pishgam Biotech Company, Iran) and a SYBR Green master mix kit (PARSTOUS, Mashhad, Iran) on an Applied Biosystems system (Thermo Fisher Scientific, Massachusetts, USA).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Subjects\u0026rsquo; recruitment\u003c/h2\u003e \u003cp\u003eThe study population consisted of 40 plasma samples and 10 tissue specimens obtained from patients with colorectal cancer (CRC). Additionally, 40 normal plasma samples and 10 normal colorectal tissue samples were collected from healthy individuals to serve as the control group. All samples were obtained at Beheshti Hospital, Kashan University of Medical Sciences, Kashan, Iran, by a college-affiliated gastroenterologist (Ethical code: IR.KAUMS.MEDNT.REC.1402.259). Before participating in the study, all individuals were thoroughly informed and provided written informed consent. The study was conducted between January 2023 and March 2025.\u003c/p\u003e \u003cp\u003eInclusion criteria were: histological confirmation of colorectal adenocarcinoma, absence of cancer or underlying diseases in healthy individuals providing blood and tissue samples, age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, and written informed consent. Exclusion criteria included: presence of other cancer types, inflammatory bowel disease, severe comorbidities, pregnancy, or refusal to provide informed consent [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. RNA extraction and cDNA synthesis\u003c/h2\u003e \u003cp\u003e For the extraction of total RNA, TRIzol reagent was added to both tissue and plasma samples, followed by vigorous shaking. Subsequently, chloroform was added. After centrifugation at 15,000 rpm for 10 minutes at 4\u0026deg;C, the mixture separated into two phases, and RNA was isolated from the aqueous phase. To assess the integrity and quantity of the extracted RNA, gel electrophoresis and a NanoDrop spectrophotometer (Thermo Fisher Scientific, One/C) were used, respectively. Complementary DNA (cDNA) synthesis was performed using reverse transcriptase enzyme and random hexamer primers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Quantitative reverse transcription polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eRT-qPCR was utilized to assess the tissue and plasma expression levels of the β-catenin gene, lncRNA RBM5-AS1, lncRNA STEAP3-AS1, and lncRNA VPS9D1-AS1 in CRC patients and healthy individuals. The final volume of each qRT-PCR reaction was 20 \u0026micro;l, which included the master mix, cDNA, forward and reverse primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and ddH2O, and was performed in triplicate. The thermal cycling protocol included an initial denaturation step at 95\u0026deg;C for 10 minutes, followed by 45 cycles consisting of denaturation at 94\u0026deg;C for 10 seconds, annealing at 63\u0026deg;C for 30 seconds, and extension at 70\u0026deg;C for 20 seconds. qRT-PCR data were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as the internal reference gene. The relative expression levels of the target genes were quantified using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method.\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\u003ePrimer sequences and their product length\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduct length (bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLncRNA RBM5-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AACAAGCCGCCTGAACTAAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: CTGATTCCCCCAGTCTTCAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLncRNA VPS9D1-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GGAAATGTGACAAGCTGCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: CGCCGTGTATACTCCGATG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLncRNA STEAP3-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CACTGCCTCCTCTTGGAAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: GCCAGAAGAGATGGACAAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCTNNB1 (β-catenin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CCTGTTCCCCTGAGGGTATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: CCATCAAATCAGCTTGAGTAGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CATGTTGCAACCGGGAAGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR: GCCCAATACGACCAAATCAGAG\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=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Histopathological examination\u003c/h2\u003e \u003cp\u003eSurgical biopsy specimens were collected from ten patients diagnosed with colorectal cancer and ten healthy individuals who had undergone colonoscopy. Approximately 0.5 g of each sample was taken, rinsed with physiological saline solution, and then frozen at -80\u0026deg;C for histological examination. Hematoxylin and eosin (H\u0026amp;E) staining was applied to ensure accurate classification, and the diagnosis of colorectal carcinoma was subsequently confirmed by an expert pathologist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS (version 16) and GraphPad Prism (version 10). Several statistical tests were applied, including the Kolmogorov-Smirnov test to assess data normality, Chi-square and independent sample t-test to evaluate differences between two groups, Spearman's test to determine the statistical relationship among different variables, and ROC curve analysis to identify the optimal cutoff values for each diagnostic parameter. In this study, a p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant, and all data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patients and samples\u003c/h2\u003e \u003cp\u003eForty plasma specimens and ten colon tissue samples were collected from both CRC patients and healthy controls. Demographic information was gathered during the sampling process. There were no significant differences between the groups regarding age, gender, or smoking status (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eDemographic data of the patients with CRC and healthy individuals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRCs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-values*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCRC\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-values\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30,\u0026nbsp;\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree of differentiation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p-value was reported based on chi-square test; weights are based on kilogram.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Tissue samples\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA blinded evaluation by an expert pathologist confirmed that all tissues from the CRC group displayed definitive malignant characteristics, including glandular disorganization, nuclear pleomorphism, and elevated mitotic activity, consistent with cancerous pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast, tissues from the control group displayed normal histological architecture, with no evidence of neoplastic transformation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Relative expression levels of β-catenin mRNA in CRC and control group.\u003c/h2\u003e \u003cp\u003eThe mRNA expression levels of β-catenin in CRC tissues were significantly higher than those in the control group (7-fold increase; p\u0026thinsp;=\u0026thinsp;0.02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, plasma β-catenin mRNA expression levels were also significantly higher in CRC patients, showing nearly a 5-fold increase compared to normal plasma samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Tissue‑specific expression of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1 in CRC compared to the control group.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTissue expression levels of lncRNA STEAP3-AS1 were significantly higher in the patient group compared to the control group (5-fold increase; p\u0026thinsp;=\u0026thinsp;0.023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, the expression of lncRNA VPS9D1-AS1 was markedly higher in CRC patients compared to healthy controls, with an approximately 4-fold increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, the expression of lncRNA RBM5-AS1 was also significantly higher in CRC patients compared to control subjects, with a 6-fold increase (p\u0026thinsp;=\u0026thinsp;0.004) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.5 Plasmaspecific Expression of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1 in CRC compared to the control group.\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePlasma expression levels of lncRNA STEAP3-AS1 were significantly higher, with approximately a 6-fold increase compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, lncRNA VPS9D1-AS1 showed significantly higher expression relative to the control group, with a 5-fold increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, the expression of lncRNA RBM5-AS1 was significantly elevated in CRC patients, with a 5-fold increase compared to the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.6 Comparison of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA expression between tissue and plasma in the CRC group.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis of lncRNA STEAP3-AS1 expression levels showed no significant differences between plasma and tissue samples in the CRC group. qRT-PCR results indicated that the relative expression levels of lncRNA STEAP3-AS1 in plasma were comparable to those in tissue samples (6.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 vs 5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9; p\u0026thinsp;=\u0026thinsp;0.26) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, the expression levels of lncRNA VPS9D1-AS1 showed no significant differences between plasma and tissue samples in the CRC group (5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 vs 4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; p\u0026thinsp;=\u0026thinsp;0.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Likewise, lncRNA RBM5-AS1 expression showed no significant variation between plasma and tissue samples in the CRC group (5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 vs 5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09; p\u0026thinsp;=\u0026thinsp;0.61) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Finally, expression analysis of β-catenin mRNA revealed no statistically significant differences between plasma and tissue samples (4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38 vs 6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18; p\u0026thinsp;=\u0026thinsp;0.07) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Subgroup Evaluation of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA in CRC Patients\u003c/h2\u003e \u003cp\u003eThe expression levels of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA were investigated in relation to pathological stages, differentiation, and other clinical parameters. No significant associations were found between the expression levels of these lncRNAs and the clinical parameters (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7. ROC curve analysis\u003c/h2\u003e \u003cp\u003eROC curve analysis was used to evaluate the diagnostic value of plasma lncRNAs in CRC patients. The predictive value of plasma β-catenin levels was determined with an area under the curve (AUC) of 0.88 (95% confidence interval (CI) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] 0.79\u0026ndash;0.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a sensitivity of 92.5% and specificity of 90.0% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Additionally, the plasma expression level of lncRNA STEAP3-AS1 demonstrated an AUC of 0.82 (95% CI: 0.72\u0026ndash;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a sensitivity of 70% and specificity of 90% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Notably, the plasma expression level of lncRNA VPS9D1-AS1 exhibited impressive diagnostic performance with an AUC of 0.94 (95% CI: 0.87\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside a sensitivity and specificity of 92.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Moreover, the AUC related to lncRNA RBM5-AS1 was 0.83 (95% CI: 0.73\u0026ndash;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a sensitivity of 90% and specificity of 77.14% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Finally, combining the plasma expression levels of lncRNA STEAP3-AS1, lncRNA VPS9D1-AS1, lncRNA RBM5-AS1, and β-catenin mRNA further improved the AUC to 0.97 (95% CI: 0.93\u0026ndash;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with 99% sensitivity and 58% specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eSubgroup analysis of LncRNA STEAP3-AS1, LncRNA VPS9D1-AS1, LncRNA RBM5-AS1, and β-catenin mRNA in CRC.\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=\"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=\"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\"\u003e \u003cp\u003eClinicopathological factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eβ-catenin mRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLncRNA STEAP3-AS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eLncRNA VPS9D1-AS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eLncRNA RBM5-AS1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChanging fold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChanging fold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChanging fold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eChanging fold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\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\u0026gt;\u0026thinsp;30, \u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\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\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\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\u003ePathological 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\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\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\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\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\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDegree of differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\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\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\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\u003eWeight\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\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\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\u0026gt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\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\u003eGender\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\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\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\"\u003e*: p-value was reported based on The Mann Whitney test for two groups and Kruskal-Wallis one-way ANOVA for more than two groups; **: measurement data were expressed plasma-specific expression levels as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;The standard error of the mean.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe area under curve and other calculated parameter for ROC analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%) (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%) (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLR+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-catenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88(0.79\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.50 (79.6\u0026ndash;98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.00 (76.3\u0026ndash;97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTEAP3-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82(0.72\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.00 (53.5\u0026ndash;83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.00 (76.3\u0026ndash;97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\u003e0.94(0.87\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.50 (79.6\u0026ndash;98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.50 (79.6\u0026ndash;98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBM5-AS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.73\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.00 (76.3\u0026ndash;97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.14 (59.9\u0026ndash;89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Correlation analysis of clinical and gene expression variables\u003c/h2\u003e \u003cp\u003eWe performed correlation coefficient analysis to assess the strength and direction of the linear relationships between variables [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. There were moderate positive correlations between age with degree of differentiation (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.59; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), pathological stage (r\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.18; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), β-catenin expression (r\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.47; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), and lncRNA STEAP3-AS1 expression (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.25; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Conversely, there were weak to moderate inverse correlations with lncRNA VPS9D1-AS1 expression (r = -0.19, p\u0026thinsp;=\u0026thinsp;0.23; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD) and lncRNA RBM5-AS1 expression (r = -0.15, p\u0026thinsp;=\u0026thinsp;0.34; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Patients' weight showed weak positive correlations with β-catenin expression (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;=\u0026thinsp;0.38; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), lncRNA STEAP3-AS1 expression (r\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.96; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG), lncRNA VPS9D1-AS1 expression (r\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.91; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH), and lncRNA RBM5-AS1 expression (r\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.09; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI). Additionally, body weight had significant positive correlations with both the degree of differentiation (r\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;=\u0026thinsp;0.019) and pathological stage (r\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.008). No significant correlations were observed between the expression levels of each pair of studied lncRNAs; however, lncRNA RBM5-AS1 exhibited a slight positive correlation with lncRNA VPS9D1-AS1 (r\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.29) and lncRNA STEAP3-AS1 (r\u0026thinsp;=\u0026thinsp;0.10, p\u0026thinsp;=\u0026thinsp;0.33). Similarly, lncRNA STEAP3-AS1 showed a slight positive correlation with lncRNA VPS9D1-AS1 (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;=\u0026thinsp;0.80).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDespite significant advances in detection and therapeutic strategies, CRC remains one of the leading causes of cancer-related mortality. This high mortality is partly due to the absence of effective early diagnostic modalities and the emergence of chemoresistance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent technological advancements have facilitated the identification of numerous dysregulated signaling pathways that contribute to the development of CRC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Additionally, some researchers have reported that aberrations in the Wnt/β-catenin signaling cascade have emerged as a critical contributor to oncogenesis, which promotes cancer development by promoting angiogenesis, metastasis, and drug resistance [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. β-catenin, a central effector in the Wnt/β-catenin signaling pathways, is frequently dysregulated in CRC. Its overactivation results in the activation of growth-promoting genes, leading to uncontrolled cell proliferation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our study specifically investigated the expression of β-catenin mRNA in CRC tissues and plasma, comparing it to a control group, in order to assess the role of β-catenin mRNA and the dysregulation of the Wnt/β-catenin signaling pathway in CRC onset, progression, and metastasis. Consistent with previous reports, our results revealed significantly higher β-catenin mRNA expression in CRC tissues compared to the control group. Furthermore, plasma-specific expression of β-catenin mRNA was also elevated. The strong interaction between lncRNAs and the Wnt/β-catenin pathway is well-documented [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The present study focused on the levels of three lncRNAs\u0026mdash;STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1\u0026mdash;and their relationship with β-catenin mRNA expression, aiming to evaluate their potential as diagnostic biomarkers in CRC patients. The results showed that the tissue expression levels of STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 were significantly higher in CRC patients compared to the control group. These findings are supported by existing evidence; under hypoxic conditions, a common feature of the solid tumor microenvironment, HIF-1α becomes stabilized and transcriptionally induces the expression of lncRNA STEAP3-AS1 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Mechanistically, lncRNA STEAP3-AS1 exerts its oncogenic effects by competitively binding to the N6-methyladenosine (m6A) 'reader' protein YTHDF2, which typically targets m6A-modified mRNAs for degradation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sequestration of YTHDF2 by lncRNA STEAP3-AS1 leads to stabilization of STEAP3 mRNA, which, in turn, increases the translation of STEAP3 protein. As a metalloreductase, STEAP3 catalyzes the conversion of ferric iron (Fe\u0026sup3;⁺) to ferrous iron (Fe\u0026sup2;⁺), resulting in the phosphorylation and subsequent inactivation of glycogen synthase kinase-3β (GSK3β) at serine-9 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Inactivation of GSK3β prevents the degradation of β-catenin, allowing its accumulation and nuclear translocation, where it activates transcriptional programs that promote cell proliferation, migration, and invasion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, in esophageal squamous cell carcinoma (ESCC), lncRNA VPS9D1‐AS1 is significantly upregulated in tumor tissues [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Functional studies in ESCC cell lines have shown that silencing lncRNA VPS9D1‐AS1 inhibits cell proliferation, migration, invasion, and colony formation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mechanistically, lncRNA VPS9D1‐AS1 appears to regulate wnt/β‐catenin signaling, as its knockdown results in decreased levels of β‐catenin and its downstream targets (e.g., c-Myc). Furthermore, the depletion of lncRNA VPS9D1‐AS1 induces G0/G1 cell cycle arrest and downregulates key cell cycle regulators (CDK4, CDK6, Cyclin D1), highlighting its role in promoting cell proliferation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe lncRNA RBM5-AS1 (also referred to as LUST) has primarily been studied in breast cancer patients, where it is significantly upregulated compared to normal tissue [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. the nucleus by suppressing AXIN1, a key component of the β‐catenin destruction complex, potentially through the recruitment of transcriptional regulators such as CTCF. This stabilization supports the assembly of the β‐catenin\u0026ndash;TCF4 transcriptional complex, leading to the increased expression of wnt target genes (e.g., c-Myc and Cyclin D1). Furthermore, hypoxic conditions may induce the expression of lncRNA RBM5‐AS1 via transcription factors such as RUNX2, thereby linking tumor hypoxia to Wnt pathway activation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite existing approval documents regarding the higher expression of lncRNAs STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 in CRC tissues compared to normal subjects, there has been limited investigation to confirm this in matched plasma samples for potential use as biomarkers. The present study, therefore, focused on measuring the plasma levels of these lncRNAs to explore their potential for early detection and monitoring of disease progression. Plasma analysis in CRC patients revealed significantly higher expression of these three lncRNAs compared to the control group. To validate these observations, we compared the tissue and plasma expression levels for each lncRNA. The results suggest a strong correlation between tissue and plasma expression.\u003c/p\u003e\u003cp\u003eROC curve analysis was conducted to assess the diagnostic potential of lncRNAs STEAP3-AS1, VPS9D1-AS1, RBM5-AS1, and the β-catenin gene individually, as well as in combination, based on AUC values. The results demonstrated that the diagnostic performance of these lncRNAs and the β-catenin gene in identifying CRC patients was both acceptable and accurate. Specifically, lncRNA VPS9D1-AS1 and the β-catenin gene exhibited the highest sensitivity, while lncRNA STEAP3-AS1 showed the lowest sensitivity. Regarding specificity, lncRNA VPS9D1-AS1 had the highest value, while lncRNA RBM5-AS1 had the lowest. Notably, lncRNA VPS9D1-AS1 outperformed the other markers in discriminating CRC patients from the control group, based on its AUC data. Additionally, the combination of all three lncRNAs and the β-catenin gene was assessed, and the results indicated that this combination improved the AUC and sensitivity, though it reduced specificity. These findings highlight the potential of STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 as promising diagnostic biomarkers for CRC.\u003c/p\u003e\u003cp\u003eTo examine potential associations between β-catenin and the expression of lncRNAs (STEAP3-AS1, VPS9D1-AS1, RBM5-AS1) with various clinical parameters, including age and weight, Spearman\u0026rsquo;s correlation analysis was conducted. The analysis revealed no significant associations between lncRNA expression and key clinicopathological factors such as tumor differentiation, stage, or patient weight. Similarly, no significant correlations were found with age. However, a significant correlation was observed between tumor differentiation and stage, as well as between body weight and both of these parameters. This association may be attributed to the well-established link between obesity and CRC. Previous studies have demonstrated that obesity is strongly associated with an increased risk of CRC, primarily due to its role in chronic inflammation and metabolic dysregulation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe results demonstrated that the expression levels of tissue and plasma lncRNAs (STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1) in CRC patients were significantly higher than those in healthy subjects. Furthermore, ROC curve analysis confirmed the diagnostic potential of these lncRNAs as well as β-catenin as biomarkers for CRC. Correlation analysis, however, showed no significant associations between the expression of these lncRNAs and patient age or weight. Nevertheless, the study has some limitations, including a relatively small sample size, reliance on a single analytical method to measure each laboratory parameter, and the absence of patient outcome data. To further validate the prognostic and diagnostic significance of these biomarkers, larger-scale studies are necessary\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Kashan University of Medical Science, Kashan, Iran\u0026nbsp;(grant numbers 402165, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Gastroenterology Department of Kashan Beheshti Hospital, the Research Center of Biochemistry and Nutrition in Metabolic Diseases, and the Anatomical Sciences Research Center at Kashan University of Medical Sciences, Kashan, Iran, for their technical support. The authors also extend their sincere gratitude to all participants involved in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData can be accessed upon request. The datasets used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMohsen Hemmati-Dinarvand conceived of the study, developed the study protocol, participated in the design of the study. Mohammadreza Karbalaee Hashemiyan participated in analyzed the data, review ROC curve chart and wrote the paper. Reza Manouchehri-Ardakani and Mohsen Razavizade contributed in sample collecting, abstracted data and reviewed the references. Ali Rafat reviewed the manuscript and advised on revisions to the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Cell Death Dis 13(2):95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-022-04536-y\u003c/span\u003e\u003cspan address=\"10.1038/s41419-022-04536-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LncRNAs, colorectal cancer, biomarker, wnt/β-catenin signaling","lastPublishedDoi":"10.21203/rs.3.rs-6534629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6534629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) continues to be a major contributor to cancer-associated deaths worldwide, largely due to late-stage diagnoses. This study aims to investigate the tissue and plasma expression levels of antisense long non-coding RNAs (lncRNAs) in CRC patients using receiver operating characteristic (ROC) analysis to evaluate their diagnostic potential.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003eThe current case-control study included an equal number of plasma (n\u0026thinsp;=\u0026thinsp;40) and tissue (n\u0026thinsp;=\u0026thinsp;10) samples from CRC patients and normal controls. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to assess the expression levels of lncRNAs RBM5-AS1, STEAP3-AS1, and VPS9D1-AS1. Additionally, the diagnostic performance of these lncRNAs was evaluated through ROC curve analysis, which also helped to identify the appropriate cutoff values.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe expression of the β-catenin gene was significantly higher in both CRC tissues (6.78-fold increase, p\u0026thinsp;=\u0026thinsp;0.02) and plasma samples (4.79-fold increase, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the control group. Similarly, the expression of the lncRNAs was significantly higher in both CRC tissues and plasma samples compared to healthy subjects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, ROC curve analysis demonstrated that these lncRNAs had strong predictive power, with AUC values of 0.82 for STEAP3-AS1, 0.94 for VPS9D1-AS1, and 0.83 for RBM5-AS1.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAs the results showed, the expression levels of β-catenin and lncRNAs STEAP3-AS1, VPS9D1-AS1, and RBM5-AS1 in both tissues and plasma samples from CRC patients were higher than those in healthy subjects; thus, they could serve as powerful biomarkers for CRC diagnosis. However, further studies are required to confirm these results and explore new approaches.\u003c/p\u003e","manuscriptTitle":"Dysregulation of LncRNA RBM5-AS1, LncRNA VPS9D1-AS1, LncRNA STEAP3-AS1, and wnt/β-catenin provides insights into colorectal cancer diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:07:41","doi":"10.21203/rs.3.rs-6534629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-15T09:29:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T07:04:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T12:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282088378823457938495782796219613084151","date":"2025-05-02T06:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78955878790558830131572338248740484829","date":"2025-04-29T18:23:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T12:50:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T07:09:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T07:09:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2025-04-26T10:56:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1feced95-73b3-466f-81a5-c993f5250da4","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T15:58:25+00:00","versionOfRecord":{"articleIdentity":"rs-6534629","link":"https://doi.org/10.1007/s11033-025-10696-9","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2025-06-16 15:56:57","publishedOnDateReadable":"June 16th, 2025"},"versionCreatedAt":"2025-05-07 09:07:41","video":"","vorDoi":"10.1007/s11033-025-10696-9","vorDoiUrl":"https://doi.org/10.1007/s11033-025-10696-9","workflowStages":[]},"version":"v1","identity":"rs-6534629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6534629","identity":"rs-6534629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}