Comprehensive analysis reveals KLB as a prognostic biomarker in colorectal cancer based on systematic pancancer analysis

Comprehensive analysis reveals KLB as a prognostic biomarker in colorectal cancer based on systematic pancancer analysis | 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 Comprehensive analysis reveals KLB as a prognostic biomarker in colorectal cancer based on systematic pancancer analysis Xiaofei Bi, Wenjin Zhang, Meimei Shen Shen, Guicheng Wu, Chengmei Fang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4085864/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The incidence of colorectal cancer, a prevalent digestive system tumor, is increasing. Earlier research has demonstrated the significant impact of β-Klotho(KLB) on the development of metabolic disorders. Nonetheless, the function of KLB in tumors, particularly in colorectal cancer(CRC), remains underresearched. Methods By employing databases such as the TCGA, GTEx, Human Protein Atlas, UALCAN, and cBioPortal, we gathered information regarding KLB expression levels, its predictive and diagnostic importance, epigenetic characteristics, various immune and molecular subtypes, immune checkpoints, and the extent of immune infiltration. The “clusterProfiler” R package was utilized for enrichment analysis to investigate the possible role of KLB. To determine the optimal prognostic model, multivariate Cox regression and Akaike's information criterion were applied. Additionally, CCK-8 assays, colony formation assays, and cell scratch assays were employed to assess the impact of KLB on the biological activities of CRC cells. Results Pancancer studies revealed a decrease in KLB in CRC and various other cancers, but an increase in KLB in liver hepatocellular carcinoma and prostate adenocarcinoma. Consequently, reduced KLB expression correlated with a lower TNM stage and unfavorable clinical outcomes in CRC patients. The nomogram, developed considering KLB, CEA level, and TNM stage, demonstrated enhanced predictive accuracy in CRC. Analysis of immune cell infiltration revealed a correlation between reduced KLB expression and decreased infiltration of immune cells. Experiments involving CCK-8, colony formation, and cell scratch assays revealed that the increased in vitro expression of KLB suppressed the growth, movement, and infiltration of CRC cells. Conclusion The expression levels of KLB were lower in CRC tissues than in normal tissues. A notable correlation was found between its reduced expression and a grim outlook. Furthermore, KLB is crucial for the immune response of tumors and the biological actions of CRC cells. Consequently, KLB could be a potential biomarker for prognosis and a target for therapy in CRC patients. KLB colorectal cancer pancancer analysis prognosis immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Global cancer data from 2020 revealed that colorectal cancer (CRC) has the third highest incidence rate, while its death rate is the second highest among various cancers, continuing to be a significant public health challenge [ 1 ] . The emergence of colorectal cancer is gradual, with common indicators showing limited sensitivity and specificity, complicating early identification [ 2 ] . Currently, the primary diagnostic techniques for colorectal cancer include colonoscopy along with nonintrusive stool and blood examinations [ 3 ][ 4 ] . While colonoscopy is effective in detecting initial lesions, it comes with its own set of risks during the procedure. Consequently, it is critically important to identify novel molecular indicators for diagnosing and treating patients with CRC. The β-Klotho (KLB) protein, a single-pass transmembrane entity, consists of extracellular C-terminal domains, an intramembranous domain, and a brief cytoplasmic domain, weighing 119. 8 kDa. Human studies have revealed protein complexity, with β-Klotho and α-Klotho proteins exhibiting approximately 41.2% amino-acid compatibility, in contrast to the 79% similarity between mouse and human versions of the KLB protein [ 5 ][ 6 ] . Currently, the function of KLB within tumors is underresearched, and its underlying mechanism is not well understood. Higher levels of KLB gene expression in uterine endometrial cancer are correlated with lower degrees of clinical staging according to FIGO, the presence of highly differentiated endometrial cancer (G1), and the absence of lymph node metastases [ 7 ] . Similarly, for non-small cell lung cancer (NSCLC), a direct correlation was observed between KLB expression and both PFS and OS. Elevated levels of KLB were found to inhibit the growth of A549 cells, as well as the initiation of G1-to-S phase arrest and apoptosis induction [ 8 ] . In contrast, there was an increase in KLB expression in patients with hepatocellular carcinoma (HCC). Inhibiting KLB in Huh7 cells led to a reduction in cell growth and a decrease in subsequent FGFR4 signaling pathway activity [ 9 ] . Within prostate cancer, KLB diminishes the Rab8A-driven regulation of exosomes and accelerates their development [ 10 ] . Consequently, our view is that the expression of KLB varies, its functions differ across various tumors, and its involvement in colorectal cancer requires further investigation. To explore the expression profile of KLB in different tumor types, we used datasets provided by the TCGA project and the GEO database. In addition to analyzing KLB expression patterns across various tumor types, our study also considered the stages of clinical disease, genetic variations, and immune system penetration. Concurrently, additional investigations were conducted regarding colorectal cancer. Our team developed a predictive model, pinpointed potential action mechanisms via enrichment analysis, and confirmed its accuracy using in vitro cell studies. In summary, the KLB could serve as an indicator of clinical outcomes and holds significant importance for diagnosing and treating tumors. Materials and methods Gene expression analysis of KLB Initially, the Human Protein Atlas (HPA) website ( https://www.proteinatlas.org/ ) facilitated the collection of KLB expression data from 54 healthy tissues. Subsequently, the imbalance in KLB expression among different cancerous and normal tissues was examined by merging normal tissue data from the GTEx database with information from The Cancer Genome Atlas (TCGA). All expression data normalization utilized RNA sequencing and clinical follow-up details for 33 cancer types, including adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma (GBM), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM), were obtained from the TCGA database, were obtained from the TCGA database. RNA-seq data, formatted in TPM, were subjected to log2 transformation followed by further analysis. Data analysis was conducted using R software (version 3.6.3), while the R package “ggplot2 (version 3.3.3)” was utilized for visual depiction. Finally, the immunohistochemical data were collected via the HPA. Survival prognosis analysis Data regarding the survival and clinical outcomes of patients with 33 different cancer types were gathered from the TCGA database. RNA sequencing data, initially in FPKM format, were transformed into TPM format and subsequently subjected to log2 transformation, ensuring the preservation of samples containing clinical data. To examine KLB expression and patient outcomes, three metrics were employed: overall survival (OS), disease-free survival (DFS), and progression-free interval (PFI). For survival analysis, Cox regression and Kaplan‒Meier (KM) methods were utilized. The packages "survival," "survminer," "ggpubr," and "forestplot" were used. Genetic alteration analysis The "Gene_Mutation" module of the Tumor Immune Estimation Resource 2.0 (TIMER2) website ( http://timer.cistrome.org/ ) was utilized to investigate the genetic mutation ratios of KLB in various tumors. The online cBioPortal database ( https://www.cbioportal.org/ ) was utilized to investigate the attributes of KLB genetic modifications. The UALCAN database ( http://ualcan.path.uab.edu/analysis.html ) was used to analyze the differences in KLB methylation between different cancers and adjacent tissues. Immune-related analysis We used the Assistant for Clinical Bioinformatics platform to obtain data on the correlations between the expression of KLB and immune cell infiltration across cancers from the TCGA database. The R software package “immunedeconv” and the TIMER algorithm were used to estimate immune cell infiltration levels. Furthermore, the expression data of 22 immune checkpoint-related genes were extracted, and correlations between KLB expression and the expression of immune checkpoint-related genes were identified. Klotho β-Related Gene Enrichment Analysis in Colorectal cancer To identify DEGs in CRC, the expression patterns (HTSeq-TPM) of groups with varying levels of KLB mRNA expression were examined using the unpaired Student’s t test in the limma package. DEGs were deemed to have a limit where |log2Fold Change| surpassed 1.5, accompanied by an adjusted P value less than 0.001. Subsequently, we conducted Gene Ontology (GO) term enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and gene enrichment analysis (GSEA) on the DEGs using ggplot2 software for visual representation and clusterProfiler software for statistical evaluation. Prognostic Model Generation and Prediction To determine the optimal prognostic model, multivariate Cox regression along with Akaike's information criterion (AIC) were utilized. Additionally, the rms package in R was used to construct a nomogram for predicting patient prognosis. Patients were classified into high- and low-risk groups based on the median risk scores. The Kaplan‒Meier method, using a two-sided log-rank test, was employed to determine the difference in survival rates between the high-risk and low-risk groups. To evaluate the accuracy of the prognostic model for predicting tumor intensity, a receiver operating characteristic (ROC) curve was generated. Tissue Specimens, Cell Lines, and Culture Ten groups of tumor and paracancerous samples from patients with untreated COAD from August 2021 to November 2021 at Chongqing University Three Gorges Hospital were selected. To prevent RNA degradation, the tissue specimens were preserved in liquid nitrogen for transportation and prolonged storage. All tissue samples were subjected to histopathology for diagnosis and were later approved for use by the hospital's ethics committee. COAD cell lines (CACO2) were acquired from the Shanghai Cell Bank of the Chinese Academy of Sciences in Shanghai, China. The cells were cultivated in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA). To achieve a density of 80–90%, the medium was incubated in a stationary environment at 37°C with 5% CO2. RNA isolation and qRT‒PCR Total RNA was isolated from tissues using RIzol reagent (TaKaRa, Tokyo, Japan). The cDNA library was generated through the application of All-In-One 5× RT MasterMix (ABM, Canada). The qPCR process utilized a real-time fluorescence quantitative PCR instrument from Analytik Jena, employing a 20 µl reaction mixture that included BlasTaqTM 2X qPCR MasterMix (ABM, Canada). Beta-actin was used as an endogenous reference gene, and the results were analyzed by the 2^-△△CT method. The primers for KLB mRNA were F: 5′-TCTGTCATCCTGTCAGCACTT-3′ and R: 5′-CCAGTCCCAATACCCCAGAAAAA-3′. Beta-actin: F: 5′-GCCGACAGGATGCAGAAGG-3′, R: 5′-TGGAAGGTGGACAGCGAGG-3′. Immunohistochemistry (IHC) After fixation, decalcification, dehydration, transparency, embedding in kerosene, dewaxing, and dehydration, the human COAD des were infused into a preheated transparent liquid for cells, which included PBS, Triton, and 30% hydrogen peroxide (H2O2), for 30 minutes. Next, the human COAD slides were incubated for a quarter hour in citric acid buffer to facilitate antigen recovery. Following exposure to 0.3% H2O2 and subsequent treatment with 5% goat serum, the slides were incubated overnight with a rabbit polyclonal antibody targeting KLB (1:100, Avivasysbio, USA). The results were achieved through the application of 3,3’-diaminobenzidine tetra hydrochloride (DBA) staining. Subsequently, the slides were subjected to 5 minutes of treatment with hematoxylin. Following the cleansing, dehydration, transparency, and gel fixation of the immune complexes, their identification was performed using a microscope. Cell Counting Kit-8 (CCK-8) analysis CCK-8 reagent was obtained from HCM (China). A total of 2×103 cells were plated in 96-well plates in advance. The assessment began after 24 hours and continued without interruption for 6 days (0, 1, 2, 3, 4, 5, and 6 days). After absorbing and discarding the medium, the CCK-8 reagent was mixed with serum-free medium in a 5 ml Eppendorf tube (CCK8 reagent: serum-free medium = 10 mL: 90 mL per well) and then transferred to 96-well plates, each containing 100 ml. Following a one-hour incubation at 37°C, the optical density (OD) at a wavelength of 450 nm was measured using a microplate. Colony formation assay Transfected colon cancer cells were treated with trypsin-collagenase to dissolve them in a single cell mixture, subsequently transferred to 6-well plates at a density of 1000 cells/well, and maintained in a 5% CO2 atmosphere at 37°C for 14 days. Initially, the cells were subjected to two PBS washes, followed by 15 minutes of staining with 2% crystal violet, and subsequently, the plate was dried at ambient temperature. The plate served to count the number of clones produced. At least three separate experiments were carried out. Wound healing assay Colon cancer cells (2.5×105 cells per well) were transfected into 6-well plates. After 12 hours, the plate's cells were marked with a 20 µL pipetting head, followed by the replacement of the serum-free medium with an inverted microscope (IX81, Olympus Company, Japan) to collect images at 0, 24, and 48 hours. Each experiment was replicated three times. Statistical analyses A pair of t test series was utilized for contrasting normal and cancerous tissues. The KM curve, Cox proportional hazard regression model, and log-rank test were used for every survival analysis. Spearman's test was utilized to assess the association between gene expression and tumor immunity. The occurrence of tumors was analyzed through chi-square tests, while various groups were evaluated using Bonferroni correction. The threshold for statistical relevance was set at p < .05. RESULTS KLB is differentially expressed in normal and tumor tissues In normal tissues and organs, KLB is highly expressed in adipose tissue, liver and pancreas (Fig. 1A).TCGA database analyses of tumor tissues from 33 cancer types revealed that KLB expression was lower in the majority of tumors (Fig. 1B). The TCGA and GTEx databases showed that KLB mRNA levels were lower in patients with BRCA, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, READ, STAD, and THCA. In contrast, KLB expression was greater in LIHC and PRAD (Fig. 1C). The protein expression levels of KLB were lower in tumor tissues in the colon, stomach, and pancreas, while that were higher in liver and prostate (Fig. 1D). Figure 1 Expression levels of KLB in normal tissues and cancer tissues. (A) KLB consensus normalized expression (NX) levels for 54 normal tissue types generated by the three transcriptomics datasets (GTEx, HPA and FANTOM5); (B) KLB mRNA expression levels in 33 different tumor types from the TCGA database via the GEPIA2 portal; (C) adding normal tissues (GETs) to compare the expression of KLB in tumor and normal tissues; (D) immunohistochemistry of KLB protein expression between tumor and normal tissues from the Human Protein Atlas (HPA) database. *P < 0.05, **P < 0.01, ** P < 0.001, *** P < 0.0001. KLB Expression is Associated with the Prognosis of Various Tumors We wanted to focus on the associations of KLB expression with prognosis,OS and clinical TNM stage. Cox regression analysis revealed a significant correlation between KLB expression and OS in patients with LAML, LGG, LUAD and PAAD (Fig. 2A). KLB was differentially expressed in the T-stage of BRCA, LUSC, PAAD and STAD and in the N-stage of COADREAD, LUAD and KIRP (Fig. 2B, 2C). KLB expression and patient prognosis were determined by GEPIA2 in different tumor patients. Higher KLB expression was associated with longer OS in LGG and PAAD patients and longer DFS in LGG, LUAD, and READ patients. Lower KLB expression was associated with longer OS in patients with DLBC, while lower KLB expression was associated with longer DFS in patients with SARC and STAD (Fig. 2D, 2E). Figure 2 Correlation between KLB expression and overall survival in patients with different TCGA tumor types. (A) Correlations between KLB expression and OS were analyzed by Cox regression using data from the TCGA database; (B, C) KLB expression levels in different clinical TNM stages of various tumors; (D, E) Overall survival (OS) and disease-free survival (DFS) of patients with different KLB expression levels in 33 cancer types according to the GEPIA2 tool. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 Alterations in the KLB Gene Are Associated with the Development and Progression of Multiple Tumors Human cancers develop as a result of the accumulation of genetic alterations. According to our analysis, the most frequent KLB alteration is “mutation”, especially in SKCM (> 6%). CHOL had the highest incidence of the “amplification” type of CNA, with a frequency of ~ 3% (Fig. 3A). Figure 3B shows the relationship between certain genetic alterations in KLB and the clinical survival prognosis of patients. We systematically studied and correlated these findings with LIHC, KIRC, and PAAD. Patients with genetic alterations in KLB had a worse OS ( P = 0.0837, P = 0.187, P = 0.130) than patients without KLB alterations. However, large-sample data are needed for verification (Fig. 3B). We further explored the promoter methylation level of KLB in various cancers in the TCGA. In BRCA, BLCA, COAD, UCEC, LIHC, KIRP, and READ, we noticed that the promoter methylation level of KLB was greater in primary tumors, and it was lower in PRAD (Fig. 3C). Figure 3 DNA methylation and mutation features of KLB across cancers. (A) The alteration frequency with different types of mutations was examined using the cBioPortal database. (B) The effect of KLB mutation status on overall disease-fre of some tumors using the cBioPortal database. (C) The promoter methylation level of KLB across cancers via the UALCAN database. Correlations between KLB, tumor immune infiltration and immune checkpoint-related genes Because of the distinct relationship between KLB and the immune response, we performed a pancancer analysis of the association between KLB expression and the immune infiltration level. Among the 24 subtypes of immune cells, KLB expression was negatively and significantly correlated with these subtypes in BLCA, GBM, LAML, LGG, LUAD, and TGCT. KLB expression was positively correlated with BRCA, LUSC, and READ (Fig. 4A). Immunosurveillance influences the prognosis of cancer patients, and tumors evade immune responses by taking advantage of immune checkpoints. Notably, we observed that the expression of KLB was positively correlated with that of most immunoinhibitors and immunostimulators in BRCA, COAD, LUSC, PRAD, READ and THCA. In contrast, the expression of KLB was negatively correlated with that of BLCA and LGG. CD160 and BTLA were most positively associated with KLB expression in these different cancers (Fig. 4B). Figure 4 KLB expression correlated with immune cells (A) and immunological checkpoint molecules (B) . ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Functional enrichment analysis of colon cancer samples with high and low KLB expression To explore the potential mechanisms of KLB, we analyzed differentially expressed genes (DEGs) in CRC samples with high and low KLB expression. A total of 939 DEGs were identified, of which 930 genes were upregulated and 9 were downregulated. The DEG expression is shown in a heatmap (Fig. 5A). Next, we performed GSEA to identify the key pathways related to KLB. GSEA revealed that the most significantly enriched pathways were Retinoblastoma Gene In Cancer, Synthesis of DNA, Mitotic Spindle Checkpoints, Mitotic G1 Phase and G1 S Transition, DNA Replication, G2 M Checkpoints, Cell Cycle Checkpoints, S Phase, Mitotic Metaphase and Anaphase and Cell Cycle Mitotic (Fig. 5B). After that, we predicted the functions of coexpressed genes in patients with KLB using GO/KEGG enrichment analysis. The top 3 GO/KEGG enrichment terms in the biological process (BP), molecular function (MF), cellular component (CC) and KEGG groups were chemical stimulus involved in sensory perception, nucleosome assembly, formation of quadruple SL/U4/U5/U6 snRNP, protein‒DNA complex, DNA packaging complex, nucleosome, olfactory receptor activity, taste receptor activity, bitter taste receptor activity, alcoholism, systemic iupus erythematosus and neutrophil extracellular trap formation (Fig. 5C, 5D). Figure 5 Differentially expressed genes and enrichment analysis of KLB in CRC. (A) Volcano plot of differentially expressed genes (DEGs). (B) GSEA enrichment analysis of KLB in CRC. (C, D) GO/KEGG enrichment analysis of KLB in CRC. Establishment of Prognostic Models for KLB In CRC Patients First, we obtained KLB expression and clinical information from the TCGA database. In terms of pathologic N stage, KLB expression differed between the N0 group and the N1 and N2 groups (Table 1 ). Through univariate and multivariate Cox regression analyses, we identified major factors affecting the progression-free interval (PFI), including the CEA level, pathological TNM stage, and KLB expression level (Table 2 ). We constructed a nomogram of PFI to integrate the KLB and other prognostic factors (Fig. 6A). A higher point on the nomogram represented a worse prognostic factor. The calibration curve was used to assess KLB's nomogram performance, with a C index of 0.778 for PFI (Fig. 6B). Overall, this nomogram may be a better model for predicting survival in KLB patients than for predicting individual prognostic factors. Table 1 Clinical characteristics of KLB patients with low and high KLB expression in the TCGA cohort (n = 644). Characteristics Low expression of KLB High expression of KLB P value n 322 322 Gender, n (%) 0.385 Male 166 (25.8%) 177 (27.5%) Female 156 (24.2%) 145 (22.5%) Age, n (%) 0.203 65 192 (29.8%) 176 (27.3%) BMI, n (%) 0.789 25 101 (30.7%) 121 (36.8%) CEA level, n (%) 0.685 5 70 (16.9%) 84 (20.2%) Pathologic T stage, n (%) 0.601 T1&T2 66 (10.3%) 65 (10.1%) T3 214 (33.4%) 222 (34.6%) T4 41 (6.4%) 33 (5.1%) Pathologic N stage, n (%) 0.047 N0 171 (26.7%) 197 (30.8%) N1&N2 148 (23.1%) 124 (19.4%) Pathologic M stage, n (%) 0.183 M0 241 (42.7%) 234 (41.5%) M1 52 (9.2%) 37 (6.6%) Table 2 Univariate and multivariate analyses of the progression-free interval (PFI) according to KLB expression. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Gender 643 0.214 Male 342 Reference Female 301 0.822 (0.602–1.121) 0.216 Age 643 0.972 65 367 1.006 (0.737–1.371) 0.972 BMI 329 0.175 25 222 1.381 (0.857–2.224) 0.185 CEA level 414 < 0.001 5 154 2.628 (1.777–3.886) < 0.001 1.736 (1.105–2.727) 0.017 Pathologic T stage 640 < 0.001 T1&T2 131 Reference Reference T3 435 2.742 (1.545–4.864) < 0.001 2.937 (1.237–6.971) 0.015 T4 74 7.590 (3.987–14.447) < 0.001 6.764 (2.556–17.898) < 0.001 Pathologic N stage 639 < 0.001 N0 367 Reference Reference N1&N2 272 2.624 (1.916–3.592) < 0.001 0.925 (0.548–1.561) 0.770 Pathologic M stage 563 < 0.001 M0 474 Reference Reference M1 89 5.577 (3.945–7.884) < 0.001 3.185 (1.824–5.559) < 0.001 KLB 643 0.053 Low 321 Reference Reference High 322 0.739 (0.543–1.005) 0.054 0.749 (0.490–1.144) 0.181 Figure 6 The prognostic value of KLB expression in CRC (A) A nomogram that integrates KLB and other prognostic factors in CRC from TCGA data; (B) The calibration curve of the nomogram. KLB inhibits the proliferation and migration of colon cancer cells By comparing the expression level of KLB in tumors with that in adjacent normal tissues, we showed that KLB was deregulated in tumor tissue. Through qPCR and IHC, we found that KLB was downregulated in tumor tissues (Fig. 7A, 7B, 7C). To verify the role of KLB in proliferation and migration, we established a CACO2 cell line stably overexpressing KLB. CCK-8, wound healing and colony formation assays showed that KLB overexpression suppressed cell proliferation and migration in vitro (Fig. 7D, 7E, 7F, 7G, 7H). Figure 7 Expression of KLB in clinical samples and the role of KLB in colon cancer cells. (A) The expression of KLB in colorectal cancer tissues and normal tissues was detected by qPCR; (B, C) the expression of KLB in colorectal cancer tissues and normal tissues was detected by IHC; (D) the effect of KLB on the proliferation of colorectal cancer cells was detected by CCK8; (E, F) the effect of KLB on the migration of colorectal cancer cells was detected by a cell scratch assay; (G, H) the effect of KLB on the proliferation of colorectal cancer cells was detected by a colony formation assay. ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001. Discussion Many past studies have indicated that KLB, acting as a coreceptor, attaches to the fibroblast growth factor receptor (FGFR), facilitating FGF21/19 (15 in mice), and is crucial for glycolipid metabolism, bile acid metabolism, and metabolic balance [ 11 ][ 12 ][ 13 ][ 14 ] . The independent effects of KLB on alcohol metabolism, food preference, tumors and reproductive development have recently received considerable attention [ 15 ][ 16 ] . Given the infrequent research on KLB in tumors, our initial step was to conduct a pancancer analysis. We found that the expression level of KLB in the tumor tissues of BRCA, CHOL, COAD, ESC, GBM, HINSC, KICH, KIRC, KIRP, LUAD, LUSC, READ, STAD and KIRP was lower than that in the corresponding control tissues, while high KLB expression was detected in LIHC and PRAD. Concurrently, patients suffering from tumors such as LGG, PAAD, LUAD, and READ experienced unfavorable outcomes when KLB was expressed at low levels. This finding aligns with earlier research findings, such as the manifestation and function of KLB in prostate cancer [ 10 ] . Increasing research suggests that genetic alterations play a role in the progression of tumors and the reaction to chemotherapy [ 17 ][ 18 ] . Several studies have reported associations between KLB variants and phenotypic outcomes or disease, such as Arg728Gln in Colonic transit at 24 hours, NAFLD and MAFLD [ 19 ][ 20 ][ 21 ] . Our study revealed that KLB alterations are common in many tumors, and KLB mutations account for the highest proportion. Alterations in the KLB gene seem to be associated with poor prognosis, but further studies with large samples are needed. DNA methylation is a significant epigenetic modification of DNA that is capable of regulating gene activity without changing the DNA sequence and is essential for gene activity, genomic stability, and tumor development [ 22 ] . In NAFLD, up-regulation of methyltransferases by high-fat diet may induce hypermethylation of the Klb promoter and subsequent down-regulation of Klb expression, resulting in the development of hepatic steatosis [ 23 ] . The results of our study showed that KLB promoter methylation was low in numerous tumors, which is contrary to the low KLB mRNA levels, suggesting that complex posttranscriptional regulatory mechanisms may be present in tumors. Numerous research findings indicate that immune cell penetration into tumor specimens influences the progression of cancerous tumors and correlates with the outlook for those with. Furthermore, new studies have revealed a link between the tumor immune microenvironment and the expression intensity of various genes [ 24 ][ 25 ][ 26 ] . This research revealed an inverse relationship between KLB expression and a range of immune cells in BLCA, GBM, LAML, LGG, LUAD, STAD, and TGC. The expression of KLB was linked to genes related to immune checkpoints. Within LGG and BLCA, a negative correlation was observed between KLB expression and the majority of genes related to immune checkpoints. This finding indicates a significant function of KLB in immunotherapy and tumor growth. Research into the function of KLB in tumors, particularly in colorectal cancer, is still limited, and the exact mechanisms driving this process remain largely elusive. Earlier studies [ 27 ][ 28 ] have pinpointed the role of KLB in the metabolism of bile acids and their transit through the colon. Comprehensive cancer research revealed a correlation between diminished KLB expression in READ and adverse outcomes. By employing univariate and multivariate Cox regression methods, we combined KLB, TNM stage, and CEA level to create a model for predicting the length of progression-free periods in CRC patients. Based on GSEA and GO/KEGG enrichment analysis, we hypothesized that KLB is involved in cell cycle, DNA structure and taste receptor activation in CRC. Ultimately, laboratory experiments showed that KLB impeded the proliferation and mobility of colon cancer cell lines. The research also encountered multiple constraints. Initially, the number of samples for some rare tumor types was limited, potentially leading to batch effects or inaccurate outcomes. Moreover, this research confirmed the significance of KLB overexpression in colon cancer cells, necessitating further experimental efforts to determine the exact molecular role of KLB in tumor development. Conclusions The expression of KLB is typically low in several cancer types, and both its expression and genetic changes are significantly correlated with clinical results in specific cancer patients. Furthermore, the study of immune infiltration and gene enrichment linked to KLB suggested a potential pathway through which KLB influences tumor immunity, cellular processes, and DNA structuring. In CRC, the increased expression of KLB was found to suppress the growth and movement of CACO2 cells in vitro. Consequently, additional experimental and clinical research is required to investigate the real-world use of KLB in cancer therapy and to predict outcomes. Abbreviations KLB：β-Klotho; CRC: colorectal cancer; TCGA: The Cancer Genome Atlas; HPA:the Human Protein Atlas; ACC:adrenocortical carcinoma; BLCA:bladder urothelial carcinoma; BRCA:breast invasive carcinoma; CESC:cervical squamous cell carcinoma; CHOL:cholangiocarcinoma; COAD:colon adenocarcinoma; DLBC:lymphoid neoplasm diffuse large B cell lymphoma; ESCA:esophageal carcinoma; GBM:glioblastoma; LGG:brain lower grade glioma; HNSC:head and neck squamous cell carcinoma; KICH:kidney chromophobe; KIRC:kidney renal clear cell carcinoma; KIRP:kidney renal papillary cell carcinoma; LAML:acute myeloid leukemia; LIHC:liver hepatocellular carcinoma; LUAD:lung adenocarcinoma; LUSC:lung squamous cell carcinoma; MESO:mesothelioma; OV:ovarian serous cystadenocarcinoma; PAAD:pancreatic adenocarcinoma; PCPG:pheochromocytoma and paraganglioma; PRAD:prostate adenocarcinoma; READ:rectum adenocarcinoma; SARC:sarcoma; SKCM:skin cutaneous melanoma; STAD:stomach adenocarcinoma; TGCT:testicular germ cell tumors; THCA:thyroid carcinoma; THYM:thymoma; UCEC:uterine corpus endometrial carcinoma; UCS:uterine carcinosarcoma; UVM:uveal melanoma; OS:overall survival; DFS:disease-free survival; PFI:progression-free interval; DEGs:differentially expressed genes; NAFLD:non-alcoholic fatty liver disease; MAFLD:Metabolic-associated fatty liver disease; FGFR: fibroblast growth factor receptor; GSEA:Gene Set Enrichment Analysis ; KEGG: Kyoto Encyclopedia of Genes and Genomes. Declarations Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant 82173359), Basic Research and Frontier Exploration Project of Chongqing and Technology Commission (cstc2018jcyjAX0181), and Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University. Ethics approval The study of patient tissue specimens was approved by the Ethics Committee of Chongqing University Three Gorges Hospital. Competing Interests The authors declare that no competinginterests exist. References Sung Hyuna,Ferlay Jacques,Siegel Rebecca L et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countrie. CA Cancer J Clin, 2021, 71: 209-249. Arnold Melina,Abnet Christian C,Neale Rachel E et al. Global Burden of 5 Major Types of Gastrointestinal Cancer .Gastroenterology, 2020, 159: 335-349. Toth Joseph F,Trivedi Mehul,Gupta Samir. Screening for Colorectal Cancer: The Role of Clinical Laboratories .Clin Chem, 2024, 70: 150-164. Yang Yong,Gao Zhaoya,Huang An et al. Epidemiology and early screening strategies for colorectal cancer in Chin.Chin J Cancer Res, 2023, 35: 606-617. Kharitonenkov Alexei,Dunbar James D,Bina Holly A et al. FGF-21/FGF-21 receptor interaction and activation is determined by betaKloth .J Cell Physiol, 2008, 215: 1-7. Ito S,Kinoshita S,Shiraishi N et al. Molecular cloning and expression analyses of mouse betaklotho, which encodes a novel Klotho family protei .Mech Dev, 2000, 98: 115-119. Wójcik-Krowiranda Katarzyna Monika,Szczepaniec Sylwia,Bieńkiewicz Andrzej. The role of the βKlotho gene in uterine endometrial cance .Ginekol Pol, 2018, 89: 563-567. Zhou Juan,Ben Suqin,Xu Tan et al. Serum β-klotho is a potential biomarker in the prediction of clinical outcomes among patients with NSCLC. J Thorac Dis, 2021, 13: 3137-3150. Poh Weijie,Wong Winnie,Ong Huimin et al. Klotho-beta overexpression as a novel target for suppressing proliferation and fibroblast growth factor receptor-4 signaling in hepatocellular carcinoma. Mol Cancer, 2012, 11: 14. Wu Tingyu,Zhang Yanshuang,Han Qing et al. Klotho-beta attenuates Rab8a-mediated exosome regulation and promotes prostate cancer progressio. Oncogene, 2023, 42: 2801-2815. Bailey Nadian N,Peterson Stephen J,Parikh Manish A et al. Pegozafermin Is a Potential Master Therapeutic Regulator in Metabolic Disorders: A Review. Cardiol Rev, 2023, dio: 10.1097/CRD.0000000000000625. Agrawal Archita,Parlee Sebastian,Perez-Tilve Diego et al. Molecular elements in FGF19 and FGF21 defining KLB/FGFR activity and specificity. Mol Metab, 2018, 13: 45-55. Geng Leiluo,Liao Boya,Jin Leigang et al. β-Klotho promotes glycolysis and glucose-stimulated insulin secretion via GP130. Nat Metab, 2022, 4: 608-626. Talukdar Saswata,Kharitonenkov Alexei. FGF19 and FGF21: In NASH we trust. Mol Metab, 2021, 46: 101152. Misrahi Micheline. β-Klotho sustains postnatal GnRH biology and spins the thread of puberty. EMBO Mol Med, 2017, 9: 1334-1337. Schumann Gunter,Liu Chunyu,O'Reilly Paul et al. KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci U S A, 2016, 113: 14372-14377. Vikova Veronika,Jourdan Michel,Robert Nicolas et al. Comprehensive characterization of the mutational landscape in multiple myeloma cell lines reveals potential drivers and pathways associated with tumor progression and drug resistance. Theranostics, 2019, 9: 540-553. Zhang Meng,Yang Heli,Fu Tao et al. Liquid biopsy: circulating tumor DNA monitors neoadjuvant chemotherapy response and prognosis in stage II/III gastric cance. Mol Oncol, 2023, 17: 1930-1942. Wong Banny S,Camilleri Michael,Carlson Paula J et al. A Klothoβ variant mediates protein stability and associates with colon transit in irritable bowel syndrome with diarrhea. Gastroenterology, 2011, 140: 1934-1942. Dongiovanni Paola,Crudele Annalisa,Panera Nadia et al. β-Klotho gene variation is associated with liver damage in children with NAFL. J Hepatol, 2020, 72: 411-419. Panera Nadia,Meroni Marica,Longo Miriam et al. The KLB rs17618244 gene variant is associated with fibrosing MAFLD by promoting hepatic stellate cell activation. EBioMedicine, 2021, 65: 103249. Mehdi Ali,Rabbani Shafaat A,Role of Methylation in Pro- and Anti-Cancer Immunity. Cancers (Basel), 2021 Feb 1;13(3):545. Wang Shirong,Zha Lin,Cui Xin et al. Epigenetic Regulation of Hepatic Lipid Metabolism by DNA Methylation. Adv Sci (Weinh), 2023, 10: e2206068. Becht Etienne,Giraldo Nicolas A,Dieu-Nosjean Marie-Caroline et al. Cancer immune contexture and immunotherapy. Curr Opin Immunol, 2016, 39: 7-13. Mao X, Xu J, Wang W et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021 Oct 11;20(1):131. Yang Yanlan,Li Huamei,Zheng Donghui et al. Immune microenvironment heterogeneity reveals distinct subtypes in neuroblastoma: insights into prognosis and therapeutic targets. Aging (Albany NY), 2023, 15: 13345-13367. Wong Banny S,Camilleri Michael,Carlson Paula J et al. A Klothoβ variant mediates protein stability and associates with colon transit in irritable bowel syndrome with diarrhea. Gastroenterology, 2011, 140: 1934-1942. Dongiovanni Paola,Crudele Annalisa,Panera Nadia et al. β-Klotho gene variation is associated with liver damage in children with NAFLD. J Hepatol, 2020, 72: 411-419. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4085864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279118308,"identity":"fe65eff2-57ab-4672-bfcb-cd7d4aa3dd71","order_by":0,"name":"Xiaofei Bi","email":"","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Bi","suffix":""},{"id":279118309,"identity":"3038f29e-a39c-420a-8987-5fdeaff64c12","order_by":1,"name":"Wenjin Zhang","email":"","orcid":"","institution":"Chongqing University Three Gorges Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjin","middleName":"","lastName":"Zhang","suffix":""},{"id":279118310,"identity":"021d4b82-360e-4f1a-a623-b4693b2d2855","order_by":2,"name":"Meimei Shen Shen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meimei","middleName":"Shen","lastName":"Shen","suffix":""},{"id":279118311,"identity":"2d579028-51a6-4e83-bc89-4cbbdfefed07","order_by":3,"name":"Guicheng Wu","email":"","orcid":"","institution":"Chongqing University Three Gorges Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guicheng","middleName":"","lastName":"Wu","suffix":""},{"id":279118312,"identity":"f41230d6-ce56-4004-a25f-9a295fb874cd","order_by":4,"name":"Chengmei Fang","email":"","orcid":"","institution":"Chongqing University Three Gorges Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chengmei","middleName":"","lastName":"Fang","suffix":""},{"id":279118313,"identity":"ae1b3474-a283-4934-b50c-99f3850dd49e","order_by":5,"name":"Jian Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfvbmgw8SfrDJ8bM3EKlFsudYssHDHj5jyZ4DRGoxuJFjJvmATS5xw40EYrWcOWAgkcBjZsxw8/HGGww1NtGEHXa8IcEgwSJNjnF2WrEFw7G03AZCWvjOHDiQkMBzzJhZOsdMgrHhMGEtDDcSGw4ksP1PbJM8Q6QWgRvJjA0JbGyJPRI8RGoBBjIzQ2IPm7EED9AvCcT4hZ+9//vPH8CotD9+eOONDzU2RPgFCQBDmxTlEC2k6hgFo2AUjIKRAQDhPENMYhBPSAAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-03-12 16:21:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4085864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4085864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52792864,"identity":"a1ef13cf-11f8-41df-97d3-42cf1b7d9a42","added_by":"auto","created_at":"2024-03-15 20:24:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression levels of KLB in normal tissues and cancer tissues. (A)\u003c/strong\u003e KLB consensus normalized expression (NX) levels for 54 normal tissue types generated by the three transcriptomics datasets (GTEx, HPA and FANTOM5); \u003cstrong\u003e(B)\u003c/strong\u003e KLB mRNA expression levels in 33 different tumor types from the TCGA database via the GEPIA2 portal; \u003cstrong\u003e(C) \u003c/strong\u003eadding normal tissues (GETs) to compare the expression of KLB in tumor and normal tissues; \u003cstrong\u003e(D) \u003c/strong\u003eimmunohistochemistry of KLB protein expression between tumor and normal tissues from the Human Protein Atlas (HPA) database. *P\u0026lt;0.05, **P\u0026lt;0.01, ** P \u0026lt; 0.001, *** P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/5fa7dd4c17c6483c7282c06c.png"},{"id":52792858,"identity":"b7b3461e-ebae-4315-8170-2d6281179ce8","added_by":"auto","created_at":"2024-03-15 20:24:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between KLB expression and overall survival in patients with different TCGA tumor types. (A) \u003c/strong\u003eCorrelations between KLB expression and OS were analyzed by Cox regression using data from the TCGA database;\u003cstrong\u003e (B, C) \u003c/strong\u003eKLB expression levels in different clinical TNM stages of various tumors; \u003cstrong\u003e(D, E) \u003c/strong\u003eOverall survival (OS) and disease-free survival (DFS) of patients with different KLB expression levels in 33 cancer types according to the GEPIA2 tool. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001, ****P\u0026lt;0.0001\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/8488d7adce00576d5a3c6d39.png"},{"id":52792861,"identity":"284e0604-af6f-4903-ae87-bb9581d6c46e","added_by":"auto","created_at":"2024-03-15 20:24:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":289560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDNA methylation and mutation features of KLB across cancers. (A) \u003c/strong\u003eThe alteration frequency with different types of mutations was examined using the cBioPortal database. \u003cstrong\u003e(B) \u003c/strong\u003eThe effect of KLB mutation status on overall disease-fre of some tumors using the cBioPortal database. \u003cstrong\u003e(C) \u003c/strong\u003eThe promoter methylation level of KLB across cancers via the UALCAN database.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/65ad1f5129e80b16095fb6b6.png"},{"id":52792862,"identity":"d35b5605-36a7-4614-8d4b-224c9d3ba428","added_by":"auto","created_at":"2024-03-15 20:24:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240004,"visible":true,"origin":"","legend":"\u003cp\u003eKLB expression correlated with immune cells\u003cstrong\u003e (A) \u003c/strong\u003eand immunological checkpoint molecules\u003cstrong\u003e (B)\u003c/strong\u003e. ∗p \u0026lt; 0.05, ∗∗p \u0026lt; 0.01, and ∗∗∗p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/e9bf114666ca68efee8c4323.png"},{"id":52792859,"identity":"f0c9b5af-57e1-4e75-b6db-537091944a21","added_by":"auto","created_at":"2024-03-15 20:24:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes and enrichment analysis of KLB in CRC.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eVolcano plot of differentially expressed genes (DEGs).\u003cstrong\u003e (B)\u003c/strong\u003e GSEA enrichment analysis of KLB in CRC. \u003cstrong\u003e(C, D) \u003c/strong\u003eGO/KEGG enrichment analysis of KLB in CRC.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/c9f6b33278bbcb2ac390615a.png"},{"id":52792865,"identity":"a1f69266-6c0f-4b98-b130-ce57093f3979","added_by":"auto","created_at":"2024-03-15 20:24:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":169793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prognostic value of KLB expression in CRC \u003c/strong\u003e\u0026nbsp;\u003cstrong\u003e(A) \u003c/strong\u003eA nomogram that integrates KLB and other prognostic factors in CRC from TCGA data;\u003cstrong\u003e (B) \u003c/strong\u003eThe calibration curve of the nomogram.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/289d291acaf3f1db230bbf98.png"},{"id":52792863,"identity":"a0532f7e-a16d-45dc-a9b7-a7805c2f5589","added_by":"auto","created_at":"2024-03-15 20:24:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":315852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression of KLB in clinical samples and the role of KLB in colon cancer cells.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The expression of KLB in colorectal cancer tissues and normal tissues was detected by qPCR;\u003cstrong\u003e(B, C)\u003c/strong\u003e the expression of KLB in colorectal cancer tissues and normal tissues was detected by IHC; \u003cstrong\u003e(D)\u003c/strong\u003e the effect of KLB on the proliferation of colorectal cancer cells was detected by CCK8;\u003cstrong\u003e (E, F) \u003c/strong\u003ethe effect of KLB on the migration of colorectal cancer cells was detected by a cell scratch assay;\u003cstrong\u003e (G, H)\u003c/strong\u003e the effect of KLB on the proliferation of colorectal cancer cells was detected by a colony formation assay. ∗p \u0026lt; 0.05, ∗∗p \u0026lt; 0.01, and ∗∗∗p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/cf21370c5a6e5ff944b8c0c7.png"},{"id":52899054,"identity":"fcea831f-0f78-424a-b1ff-40a03e9d37b2","added_by":"auto","created_at":"2024-03-18 13:37:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4085864/v1/361ae52c-eff9-4824-8c61-9eb5a997e383.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComprehensive analysis reveals KLB as a prognostic biomarker in colorectal cancer based on systematic pancancer analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobal cancer data from 2020 revealed that colorectal cancer (CRC) has the third highest incidence rate, while its death rate is the second highest among various cancers, continuing to be a significant public health challenge\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The emergence of colorectal cancer is gradual, with common indicators showing limited sensitivity and specificity, complicating early identification \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Currently, the primary diagnostic techniques for colorectal cancer include colonoscopy along with nonintrusive stool and blood examinations \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. While colonoscopy is effective in detecting initial lesions, it comes with its own set of risks during the procedure. Consequently, it is critically important to identify novel molecular indicators for diagnosing and treating patients with CRC.\u003c/p\u003e \u003cp\u003eThe β-Klotho (KLB) protein, a single-pass transmembrane entity, consists of extracellular C-terminal domains, an intramembranous domain, and a brief cytoplasmic domain, weighing 119. 8 kDa. Human studies have revealed protein complexity, with β-Klotho and α-Klotho proteins exhibiting approximately 41.2% amino-acid compatibility, in contrast to the 79% similarity between mouse and human versions of the KLB protein \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Currently, the function of KLB within tumors is underresearched, and its underlying mechanism is not well understood. Higher levels of KLB gene expression in uterine endometrial cancer are correlated with lower degrees of clinical staging according to FIGO, the presence of highly differentiated endometrial cancer (G1), and the absence of lymph node metastases \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Similarly, for non-small cell lung cancer (NSCLC), a direct correlation was observed between KLB expression and both PFS and OS. Elevated levels of KLB were found to inhibit the growth of A549 cells, as well as the initiation of G1-to-S phase arrest and apoptosis induction \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. In contrast, there was an increase in KLB expression in patients with hepatocellular carcinoma (HCC). Inhibiting KLB in Huh7 cells led to a reduction in cell growth and a decrease in subsequent FGFR4 signaling pathway activity \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Within prostate cancer, KLB diminishes the Rab8A-driven regulation of exosomes and accelerates their development \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Consequently, our view is that the expression of KLB varies, its functions differ across various tumors, and its involvement in colorectal cancer requires further investigation.\u003c/p\u003e \u003cp\u003eTo explore the expression profile of KLB in different tumor types, we used datasets provided by the TCGA project and the GEO database. In addition to analyzing KLB expression patterns across various tumor types, our study also considered the stages of clinical disease, genetic variations, and immune system penetration. Concurrently, additional investigations were conducted regarding colorectal cancer. Our team developed a predictive model, pinpointed potential action mechanisms via enrichment analysis, and confirmed its accuracy using in vitro cell studies. In summary, the KLB could serve as an indicator of clinical outcomes and holds significant importance for diagnosing and treating tumors.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene expression analysis of KLB\u003c/h2\u003e \u003cp\u003eInitially, the Human Protein Atlas (HPA) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) facilitated the collection of KLB expression data from 54 healthy tissues. Subsequently, the imbalance in KLB expression among different cancerous and normal tissues was examined by merging normal tissue data from the GTEx database with information from The Cancer Genome Atlas (TCGA). All expression data normalization utilized RNA sequencing and clinical follow-up details for 33 cancer types, including adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma (GBM), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM), were obtained from the TCGA database, were obtained from the TCGA database. RNA-seq data, formatted in TPM, were subjected to log2 transformation followed by further analysis. Data analysis was conducted using R software (version 3.6.3), while the R package \u0026ldquo;ggplot2 (version 3.3.3)\u0026rdquo; was utilized for visual depiction. Finally, the immunohistochemical data were collected via the HPA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvival prognosis analysis\u003c/h2\u003e \u003cp\u003eData regarding the survival and clinical outcomes of patients with 33 different cancer types were gathered from the TCGA database. RNA sequencing data, initially in FPKM format, were transformed into TPM format and subsequently subjected to log2 transformation, ensuring the preservation of samples containing clinical data. To examine KLB expression and patient outcomes, three metrics were employed: overall survival (OS), disease-free survival (DFS), and progression-free interval (PFI). For survival analysis, Cox regression and Kaplan‒Meier (KM) methods were utilized. The packages \"survival,\" \"survminer,\" \"ggpubr,\" and \"forestplot\" were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration analysis\u003c/h2\u003e \u003cp\u003eThe \"Gene_Mutation\" module of the Tumor Immune Estimation Resource 2.0 (TIMER2) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to investigate the genetic mutation ratios of KLB in various tumors. The online cBioPortal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to investigate the attributes of KLB genetic modifications. The UALCAN database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/analysis.html\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu/analysis.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze the differences in KLB methylation between different cancers and adjacent tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune-related analysis\u003c/h2\u003e \u003cp\u003eWe used the Assistant for Clinical Bioinformatics platform to obtain data on the correlations between the expression of KLB and immune cell infiltration across cancers from the TCGA database. The R software package \u0026ldquo;immunedeconv\u0026rdquo; and the TIMER algorithm were used to estimate immune cell infiltration levels. Furthermore, the expression data of 22 immune checkpoint-related genes were extracted, and correlations between KLB expression and the expression of immune checkpoint-related genes were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eKlotho β-Related Gene Enrichment Analysis in Colorectal cancer\u003c/h2\u003e \u003cp\u003eTo identify DEGs in CRC, the expression patterns (HTSeq-TPM) of groups with varying levels of KLB mRNA expression were examined using the unpaired Student\u0026rsquo;s t test in the limma package. DEGs were deemed to have a limit where |log2Fold Change| surpassed 1.5, accompanied by an adjusted P value less than 0.001. Subsequently, we conducted Gene Ontology (GO) term enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and gene enrichment analysis (GSEA) on the DEGs using ggplot2 software for visual representation and clusterProfiler software for statistical evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Model Generation and Prediction\u003c/h2\u003e \u003cp\u003eTo determine the optimal prognostic model, multivariate Cox regression along with Akaike's information criterion (AIC) were utilized. Additionally, the rms package in R was used to construct a nomogram for predicting patient prognosis. Patients were classified into high- and low-risk groups based on the median risk scores. The Kaplan‒Meier method, using a two-sided log-rank test, was employed to determine the difference in survival rates between the high-risk and low-risk groups. To evaluate the accuracy of the prognostic model for predicting tumor intensity, a receiver operating characteristic (ROC) curve was generated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTissue Specimens, Cell Lines, and Culture\u003c/h2\u003e \u003cp\u003eTen groups of tumor and paracancerous samples from patients with untreated COAD from August 2021 to November 2021 at Chongqing University Three Gorges Hospital were selected. To prevent RNA degradation, the tissue specimens were preserved in liquid nitrogen for transportation and prolonged storage. All tissue samples were subjected to histopathology for diagnosis and were later approved for use by the hospital's ethics committee.\u003c/p\u003e \u003cp\u003eCOAD cell lines (CACO2) were acquired from the Shanghai Cell Bank of the Chinese Academy of Sciences in Shanghai, China. The cells were cultivated in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA). To achieve a density of 80\u0026ndash;90%, the medium was incubated in a stationary environment at 37\u0026deg;C with 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and qRT‒PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from tissues using RIzol reagent (TaKaRa, Tokyo, Japan). The cDNA library was generated through the application of All-In-One 5\u0026times; RT MasterMix (ABM, Canada). The qPCR process utilized a real-time fluorescence quantitative PCR instrument from Analytik Jena, employing a 20 \u0026micro;l reaction mixture that included BlasTaqTM 2X qPCR MasterMix (ABM, Canada). Beta-actin was used as an endogenous reference gene, and the results were analyzed by the 2^-△△CT method. The primers for KLB mRNA were F: 5\u0026prime;-TCTGTCATCCTGTCAGCACTT-3\u0026prime; and R: 5\u0026prime;-CCAGTCCCAATACCCCAGAAAAA-3\u0026prime;. Beta-actin: F: 5\u0026prime;-GCCGACAGGATGCAGAAGG-3\u0026prime;, R: 5\u0026prime;-TGGAAGGTGGACAGCGAGG-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry (IHC)\u003c/h2\u003e \u003cp\u003eAfter fixation, decalcification, dehydration, transparency, embedding in kerosene, dewaxing, and dehydration, the human COAD des were infused into a preheated transparent liquid for cells, which included PBS, Triton, and 30% hydrogen peroxide (H2O2), for 30 minutes. Next, the human COAD slides were incubated for a quarter hour in citric acid buffer to facilitate antigen recovery. Following exposure to 0.3% H2O2 and subsequent treatment with 5% goat serum, the slides were incubated overnight with a rabbit polyclonal antibody targeting KLB (1:100, Avivasysbio, USA). The results were achieved through the application of 3,3\u0026rsquo;-diaminobenzidine tetra hydrochloride (DBA) staining. Subsequently, the slides were subjected to 5 minutes of treatment with hematoxylin. Following the cleansing, dehydration, transparency, and gel fixation of the immune complexes, their identification was performed using a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK-8) analysis\u003c/h2\u003e \u003cp\u003eCCK-8 reagent was obtained from HCM (China). A total of 2\u0026times;103 cells were plated in 96-well plates in advance. The assessment began after 24 hours and continued without interruption for 6 days (0, 1, 2, 3, 4, 5, and 6 days). After absorbing and discarding the medium, the CCK-8 reagent was mixed with serum-free medium in a 5 ml Eppendorf tube (CCK8 reagent: serum-free medium\u0026thinsp;=\u0026thinsp;10 mL: 90 mL per well) and then transferred to 96-well plates, each containing 100 ml. Following a one-hour incubation at 37\u0026deg;C, the optical density (OD) at a wavelength of 450 nm was measured using a microplate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eTransfected colon cancer cells were treated with trypsin-collagenase to dissolve them in a single cell mixture, subsequently transferred to 6-well plates at a density of 1000 cells/well, and maintained in a 5% CO2 atmosphere at 37\u0026deg;C for 14 days. Initially, the cells were subjected to two PBS washes, followed by 15 minutes of staining with 2% crystal violet, and subsequently, the plate was dried at ambient temperature. The plate served to count the number of clones produced. At least three separate experiments were carried out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWound healing assay\u003c/h2\u003e \u003cp\u003eColon cancer cells (2.5\u0026times;105 cells per well) were transfected into 6-well plates. After 12 hours, the plate's cells were marked with a 20 \u0026micro;L pipetting head, followed by the replacement of the serum-free medium with an inverted microscope (IX81, Olympus Company, Japan) to collect images at 0, 24, and 48 hours. Each experiment was replicated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eA pair of t test series was utilized for contrasting normal and cancerous tissues. The KM curve, Cox proportional hazard regression model, and log-rank test were used for every survival analysis. Spearman's test was utilized to assess the association between gene expression and tumor immunity. The occurrence of tumors was analyzed through chi-square tests, while various groups were evaluated using Bonferroni correction. The threshold for statistical relevance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eKLB is differentially expressed in normal and tumor tissues\u003c/h2\u003e \u003cp\u003eIn normal tissues and organs, KLB is highly expressed in adipose tissue, liver and pancreas (Fig.\u0026nbsp;1A).TCGA database analyses of tumor tissues from 33 cancer types revealed that KLB expression was lower in the majority of tumors (Fig.\u0026nbsp;1B). The TCGA and GTEx databases showed that KLB mRNA levels were lower in patients with BRCA, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, READ, STAD, and THCA. In contrast, KLB expression was greater in LIHC and PRAD (Fig.\u0026nbsp;1C). The protein expression levels of KLB were lower in tumor tissues in the colon, stomach, and pancreas, while that were higher in liver and prostate (Fig.\u0026nbsp;1D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 1 Expression levels of KLB in normal tissues and cancer tissues. (A)\u003c/b\u003e KLB consensus normalized expression (NX) levels for 54 normal tissue types generated by the three transcriptomics datasets (GTEx, HPA and FANTOM5); \u003cb\u003e(B)\u003c/b\u003e KLB mRNA expression levels in 33 different tumor types from the TCGA database via the GEPIA2 portal; \u003cb\u003e(C)\u003c/b\u003e adding normal tissues (GETs) to compare the expression of KLB in tumor and normal tissues; \u003cb\u003e(D)\u003c/b\u003e immunohistochemistry of KLB protein expression between tumor and normal tissues from the Human Protein Atlas (HPA) database. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, *** P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\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=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eKLB Expression is Associated with the Prognosis of Various Tumors\u003c/h2\u003e \u003cp\u003eWe wanted to focus on the associations of KLB expression with prognosis,OS and clinical TNM stage. Cox regression analysis revealed a significant correlation between KLB expression and OS in patients with LAML, LGG, LUAD and PAAD (Fig.\u0026nbsp;2A). KLB was differentially expressed in the T-stage of BRCA, LUSC, PAAD and STAD and in the N-stage of COADREAD, LUAD and KIRP (Fig.\u0026nbsp;2B, 2C). KLB expression and patient prognosis were determined by GEPIA2 in different tumor patients. Higher KLB expression was associated with longer OS in LGG and PAAD patients and longer DFS in LGG, LUAD, and READ patients. Lower KLB expression was associated with longer OS in patients with DLBC, while lower KLB expression was associated with longer DFS in patients with SARC and STAD (Fig.\u0026nbsp;2D, 2E).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 2 Correlation between KLB expression and overall survival in patients with different TCGA tumor types. (A)\u003c/b\u003e Correlations between KLB expression and OS were analyzed by Cox regression using data from the TCGA database; \u003cb\u003e(B, C)\u003c/b\u003e KLB expression levels in different clinical TNM stages of various tumors; \u003cb\u003e(D, E)\u003c/b\u003e Overall survival (OS) and disease-free survival (DFS) of patients with different KLB expression levels in 33 cancer types according to the GEPIA2 tool. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in the KLB Gene Are Associated with the Development and Progression of Multiple Tumors\u003c/h2\u003e \u003cp\u003eHuman cancers develop as a result of the accumulation of genetic alterations. According to our analysis, the most frequent KLB alteration is \u0026ldquo;mutation\u0026rdquo;, especially in SKCM (\u0026gt;\u0026thinsp;6%). CHOL had the highest incidence of the \u0026ldquo;amplification\u0026rdquo; type of CNA, with a frequency of ~\u0026thinsp;3% (Fig.\u0026nbsp;3A). Figure\u0026nbsp;3B shows the relationship between certain genetic alterations in KLB and the clinical survival prognosis of patients. We systematically studied and correlated these findings with LIHC, KIRC, and PAAD. Patients with genetic alterations in KLB had a worse OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0837, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.187, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130) than patients without KLB alterations. However, large-sample data are needed for verification (Fig.\u0026nbsp;3B). We further explored the promoter methylation level of KLB in various cancers in the TCGA. In BRCA, BLCA, COAD, UCEC, LIHC, KIRP, and READ, we noticed that the promoter methylation level of KLB was greater in primary tumors, and it was lower in PRAD (Fig.\u0026nbsp;3C).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 3 DNA methylation and mutation features of KLB across cancers. (A)\u003c/b\u003e The alteration frequency with different types of mutations was examined using the cBioPortal database. \u003cb\u003e(B)\u003c/b\u003e The effect of KLB mutation status on overall disease-fre of some tumors using the cBioPortal database. \u003cb\u003e(C)\u003c/b\u003e The promoter methylation level of KLB across cancers via the UALCAN database.\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=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between KLB, tumor immune infiltration and immune checkpoint-related genes\u003c/h2\u003e \u003cp\u003eBecause of the distinct relationship between KLB and the immune response, we performed a pancancer analysis of the association between KLB expression and the immune infiltration level. Among the 24 subtypes of immune cells, KLB expression was negatively and significantly correlated with these subtypes in BLCA, GBM, LAML, LGG, LUAD, and TGCT. KLB expression was positively correlated with BRCA, LUSC, and READ (Fig.\u0026nbsp;4A).\u003c/p\u003e \u003cp\u003eImmunosurveillance influences the prognosis of cancer patients, and tumors evade immune responses by taking advantage of immune checkpoints. Notably, we observed that the expression of KLB was positively correlated with that of most immunoinhibitors and immunostimulators in BRCA, COAD, LUSC, PRAD, READ and THCA. In contrast, the expression of KLB was negatively correlated with that of BLCA and LGG. CD160 and BTLA were most positively associated with KLB expression in these different cancers (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 4\u003c/b\u003e KLB expression correlated with immune cells \u003cb\u003e(A)\u003c/b\u003e and immunological checkpoint molecules \u003cb\u003e(B)\u003c/b\u003e. \u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and \u0026lowast;\u0026lowast;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of colon cancer samples with high and low KLB expression\u003c/h2\u003e \u003cp\u003eTo explore the potential mechanisms of KLB, we analyzed differentially expressed genes (DEGs) in CRC samples with high and low KLB expression. A total of 939 DEGs were identified, of which 930 genes were upregulated and 9 were downregulated. The DEG expression is shown in a heatmap (Fig.\u0026nbsp;5A). Next, we performed GSEA to identify the key pathways related to KLB. GSEA revealed that the most significantly enriched pathways were Retinoblastoma Gene In Cancer, Synthesis of DNA, Mitotic Spindle Checkpoints, Mitotic G1 Phase and G1 S Transition, DNA Replication, G2 M Checkpoints, Cell Cycle Checkpoints, S Phase, Mitotic Metaphase and Anaphase and Cell Cycle Mitotic (Fig.\u0026nbsp;5B). After that, we predicted the functions of coexpressed genes in patients with KLB using GO/KEGG enrichment analysis. The top 3 GO/KEGG enrichment terms in the biological process (BP), molecular function (MF), cellular component (CC) and KEGG groups were chemical stimulus involved in sensory perception, nucleosome assembly, formation of quadruple SL/U4/U5/U6 snRNP, protein‒DNA complex, DNA packaging complex, nucleosome, olfactory receptor activity, taste receptor activity, bitter taste receptor activity, alcoholism, systemic iupus erythematosus and neutrophil extracellular trap formation (Fig.\u0026nbsp;5C, 5D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 5 Differentially expressed genes and enrichment analysis of KLB in CRC. (A)\u003c/b\u003e Volcano plot of differentially expressed genes (DEGs). \u003cb\u003e(B)\u003c/b\u003e GSEA enrichment analysis of KLB in CRC. \u003cb\u003e(C, D)\u003c/b\u003e GO/KEGG enrichment analysis of KLB in CRC.\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of Prognostic Models for KLB In CRC Patients\u003c/h2\u003e \u003cp\u003eFirst, we obtained KLB expression and clinical information from the TCGA database. In terms of pathologic N stage, KLB expression differed between the N0 group and the N1 and N2 groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through univariate and multivariate Cox regression analyses, we identified major factors affecting the progression-free interval (PFI), including the CEA level, pathological TNM stage, and KLB expression level (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We constructed a nomogram of PFI to integrate the KLB and other prognostic factors (Fig.\u0026nbsp;6A). A higher point on the nomogram represented a worse prognostic factor. The calibration curve was used to assess KLB's nomogram performance, with a C index of 0.778 for PFI (Fig.\u0026nbsp;6B). Overall, this nomogram may be a better model for predicting survival in KLB patients than for predicting individual prognostic factors.\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\u003eClinical characteristics of KLB patients with low and high KLB expression in the TCGA cohort (n\u0026thinsp;=\u0026thinsp;644).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\" colname=\"c2\"\u003e \u003cp\u003eLow expression of KLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of KLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEA level, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic T stage, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic N stage, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u0026amp;N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic M stage, n (%)\u003c/b\u003e\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eUnivariate and multivariate analyses of the progression-free interval (PFI) according to KLB expression.\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=\"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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822 (0.602\u0026ndash;1.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.216\u003c/p\u003e \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\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \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;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.006 (0.737\u0026ndash;1.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \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\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \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;= 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.381 (0.857\u0026ndash;2.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \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\u003cb\u003eCEA level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \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;= 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\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\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.628 (1.777\u0026ndash;3.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.736 (1.105\u0026ndash;2.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic T stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \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\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\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\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.742 (1.545\u0026ndash;4.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.937 (1.237\u0026ndash;6.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.590 (3.987\u0026ndash;14.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.764 (2.556\u0026ndash;17.898)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic N stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \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\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\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\u003eN1\u0026amp;N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.624 (1.916\u0026ndash;3.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.925 (0.548\u0026ndash;1.561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathologic M stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \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\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\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\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.577 (3.945\u0026ndash;7.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.185 (1.824\u0026ndash;5.559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKLB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \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\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \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 \u003cp\u003eReference\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\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739 (0.543\u0026ndash;1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.749 (0.490\u0026ndash;1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 6 The prognostic value of KLB expression in CRC (A)\u003c/b\u003e A nomogram that integrates KLB and other prognostic factors in CRC from TCGA data; \u003cb\u003e(B)\u003c/b\u003e The calibration curve of the nomogram.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eKLB inhibits the proliferation and migration of colon cancer cells\u003c/h2\u003e \u003cp\u003eBy comparing the expression level of KLB in tumors with that in adjacent normal tissues, we showed that KLB was deregulated in tumor tissue. Through qPCR and IHC, we found that KLB was downregulated in tumor tissues (Fig.\u0026nbsp;7A, 7B, 7C). To verify the role of KLB in proliferation and migration, we established a CACO2 cell line stably overexpressing KLB. CCK-8, wound healing and colony formation assays showed that KLB overexpression suppressed cell proliferation and migration in vitro (Fig.\u0026nbsp;7D, 7E, 7F, 7G, 7H).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFigure 7 Expression of KLB in clinical samples and the role of KLB in colon cancer cells. (A)\u003c/b\u003e The expression of KLB in colorectal cancer tissues and normal tissues was detected by qPCR; \u003cb\u003e(B, C)\u003c/b\u003e the expression of KLB in colorectal cancer tissues and normal tissues was detected by IHC; \u003cb\u003e(D)\u003c/b\u003e the effect of KLB on the proliferation of colorectal cancer cells was detected by CCK8; \u003cb\u003e(E, F)\u003c/b\u003e the effect of KLB on the migration of colorectal cancer cells was detected by a cell scratch assay; \u003cb\u003e(G, H)\u003c/b\u003e the effect of KLB on the proliferation of colorectal cancer cells was detected by a colony formation assay. \u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lowast;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and \u0026lowast;\u0026lowast;\u0026lowast;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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 \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMany past studies have indicated that KLB, acting as a coreceptor, attaches to the fibroblast growth factor receptor (FGFR), facilitating FGF21/19 (15 in mice), and is crucial for glycolipid metabolism, bile acid metabolism, and metabolic balance \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The independent effects of KLB on alcohol metabolism, food preference, tumors and reproductive development have recently received considerable attention \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Given the infrequent research on KLB in tumors, our initial step was to conduct a pancancer analysis. We found that the expression level of KLB in the tumor tissues of BRCA, CHOL, COAD, ESC, GBM, HINSC, KICH, KIRC, KIRP, LUAD, LUSC, READ, STAD and KIRP was lower than that in the corresponding control tissues, while high KLB expression was detected in LIHC and PRAD. Concurrently, patients suffering from tumors such as LGG, PAAD, LUAD, and READ experienced unfavorable outcomes when KLB was expressed at low levels. This finding aligns with earlier research findings, such as the manifestation and function of KLB in prostate cancer \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIncreasing research suggests that genetic alterations play a role in the progression of tumors and the reaction to chemotherapy \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Several studies have reported associations between KLB variants and phenotypic outcomes or disease, such as Arg728Gln in Colonic transit at 24 hours, NAFLD and MAFLD \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Our study revealed that KLB alterations are common in many tumors, and KLB mutations account for the highest proportion. Alterations in the KLB gene seem to be associated with poor prognosis, but further studies with large samples are needed. DNA methylation is a significant epigenetic modification of DNA that is capable of regulating gene activity without changing the DNA sequence and is essential for gene activity, genomic stability, and tumor development \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In NAFLD, up-regulation of methyltransferases by high-fat diet may induce hypermethylation of the Klb promoter and subsequent down-regulation of Klb expression, resulting in the development of hepatic steatosis \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The results of our study showed that KLB promoter methylation was low in numerous tumors, which is contrary to the low KLB mRNA levels, suggesting that complex posttranscriptional regulatory mechanisms may be present in tumors.\u003c/p\u003e \u003cp\u003eNumerous research findings indicate that immune cell penetration into tumor specimens influences the progression of cancerous tumors and correlates with the outlook for those with. Furthermore, new studies have revealed a link between the tumor immune microenvironment and the expression intensity of various genes \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This research revealed an inverse relationship between KLB expression and a range of immune cells in BLCA, GBM, LAML, LGG, LUAD, STAD, and TGC. The expression of KLB was linked to genes related to immune checkpoints. Within LGG and BLCA, a negative correlation was observed between KLB expression and the majority of genes related to immune checkpoints. This finding indicates a significant function of KLB in immunotherapy and tumor growth.\u003c/p\u003e \u003cp\u003eResearch into the function of KLB in tumors, particularly in colorectal cancer, is still limited, and the exact mechanisms driving this process remain largely elusive. Earlier studies \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e have pinpointed the role of KLB in the metabolism of bile acids and their transit through the colon. Comprehensive cancer research revealed a correlation between diminished KLB expression in READ and adverse outcomes. By employing univariate and multivariate Cox regression methods, we combined KLB, TNM stage, and CEA level to create a model for predicting the length of progression-free periods in CRC patients. Based on GSEA and GO/KEGG enrichment analysis, we hypothesized that KLB is involved in cell cycle, DNA structure and taste receptor activation in CRC. Ultimately, laboratory experiments showed that KLB impeded the proliferation and mobility of colon cancer cell lines.\u003c/p\u003e \u003cp\u003eThe research also encountered multiple constraints. Initially, the number of samples for some rare tumor types was limited, potentially leading to batch effects or inaccurate outcomes. Moreover, this research confirmed the significance of KLB overexpression in colon cancer cells, necessitating further experimental efforts to determine the exact molecular role of KLB in tumor development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe expression of KLB is typically low in several cancer types, and both its expression and genetic changes are significantly correlated with clinical results in specific cancer patients. Furthermore, the study of immune infiltration and gene enrichment linked to KLB suggested a potential pathway through which KLB influences tumor immunity, cellular processes, and DNA structuring. In CRC, the increased expression of KLB was found to suppress the growth and movement of CACO2 cells in vitro. Consequently, additional experimental and clinical research is required to investigate the real-world use of KLB in cancer therapy and to predict outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eKLB：\u0026beta;-Klotho; CRC: colorectal cancer; TCGA: The Cancer Genome Atlas; HPA:the Human Protein Atlas; ACC:adrenocortical carcinoma; BLCA:bladder urothelial carcinoma; BRCA:breast invasive carcinoma; CESC:cervical squamous cell carcinoma; CHOL:cholangiocarcinoma; COAD:colon adenocarcinoma; DLBC:lymphoid neoplasm diffuse large B cell lymphoma; ESCA:esophageal carcinoma; GBM:glioblastoma; LGG:brain lower grade glioma; HNSC:head and neck squamous cell carcinoma; KICH:kidney chromophobe; KIRC:kidney renal clear cell carcinoma; KIRP:kidney renal papillary cell carcinoma; LAML:acute myeloid leukemia; LIHC:liver hepatocellular carcinoma; LUAD:lung adenocarcinoma; LUSC:lung squamous cell carcinoma; MESO:mesothelioma; OV:ovarian serous cystadenocarcinoma; PAAD:pancreatic adenocarcinoma; PCPG:pheochromocytoma and paraganglioma; PRAD:prostate adenocarcinoma; READ:rectum adenocarcinoma; SARC:sarcoma; SKCM:skin cutaneous melanoma; STAD:stomach adenocarcinoma; TGCT:testicular germ cell tumors; THCA:thyroid carcinoma; THYM:thymoma; UCEC:uterine corpus endometrial carcinoma; UCS:uterine carcinosarcoma; UVM:uveal melanoma; OS:overall survival; DFS:disease-free survival; PFI:progression-free interval; DEGs:differentially expressed genes; NAFLD:non-alcoholic fatty liver disease; MAFLD:Metabolic-associated fatty liver disease; FGFR: fibroblast growth factor receptor; GSEA:Gene Set Enrichment Analysis ; KEGG: Kyoto Encyclopedia of Genes and Genomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant 82173359), Basic Research and Frontier Exploration Project of Chongqing and Technology Commission (cstc2018jcyjAX0181), and Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study of patient tissue specimens was approved by the Ethics Committee of Chongqing University Three Gorges Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no competinginterests exist.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung Hyuna,Ferlay Jacques,Siegel Rebecca L et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countrie. CA Cancer J Clin, 2021, 71: 209-249.\u003c/li\u003e\n\u003cli\u003eArnold Melina,Abnet Christian C,Neale Rachel E et al. Global Burden of 5 Major Types of Gastrointestinal Cancer .Gastroenterology, 2020, 159: 335-349.\u003c/li\u003e\n\u003cli\u003eToth Joseph F,Trivedi Mehul,Gupta Samir. Screening for Colorectal Cancer: The Role of Clinical Laboratories .Clin Chem, 2024, 70: 150-164.\u003c/li\u003e\n\u003cli\u003eYang Yong,Gao Zhaoya,Huang An et al. Epidemiology and early screening strategies for colorectal cancer in Chin.Chin J Cancer Res, 2023, 35: 606-617.\u003c/li\u003e\n\u003cli\u003eKharitonenkov Alexei,Dunbar James D,Bina Holly A et al. FGF-21/FGF-21 receptor interaction and activation is determined by betaKloth .J Cell Physiol, 2008, 215: 1-7.\u003c/li\u003e\n\u003cli\u003eIto S,Kinoshita S,Shiraishi N et al. Molecular cloning and expression analyses of mouse betaklotho, which encodes a novel Klotho family protei .Mech Dev, 2000, 98: 115-119.\u003c/li\u003e\n\u003cli\u003eW\u0026oacute;jcik-Krowiranda Katarzyna Monika,Szczepaniec Sylwia,Bieńkiewicz Andrzej. The role of the \u0026beta;Klotho gene in uterine endometrial cance .Ginekol Pol, 2018, 89: 563-567.\u003c/li\u003e\n\u003cli\u003eZhou Juan,Ben Suqin,Xu Tan et al. Serum \u0026beta;-klotho is a potential biomarker in the prediction of clinical outcomes among patients with NSCLC. J Thorac Dis, 2021, 13: 3137-3150.\u003c/li\u003e\n\u003cli\u003ePoh Weijie,Wong Winnie,Ong Huimin et al. Klotho-beta overexpression as a novel target for suppressing proliferation and fibroblast growth factor receptor-4 signaling in hepatocellular carcinoma. Mol Cancer, 2012, 11: 14.\u003c/li\u003e\n\u003cli\u003eWu Tingyu,Zhang Yanshuang,Han Qing et al. Klotho-beta attenuates Rab8a-mediated exosome regulation and promotes prostate cancer progressio. Oncogene, 2023, 42: 2801-2815.\u003c/li\u003e\n\u003cli\u003eBailey Nadian N,Peterson Stephen J,Parikh Manish A et al. Pegozafermin Is a Potential Master Therapeutic Regulator in Metabolic Disorders: A Review. Cardiol Rev, 2023, dio: 10.1097/CRD.0000000000000625.\u003c/li\u003e\n\u003cli\u003eAgrawal Archita,Parlee Sebastian,Perez-Tilve Diego et al. Molecular elements in FGF19 and FGF21 defining KLB/FGFR activity and specificity. Mol Metab, 2018, 13: 45-55.\u003c/li\u003e\n\u003cli\u003eGeng Leiluo,Liao Boya,Jin Leigang et al. \u0026beta;-Klotho promotes glycolysis and glucose-stimulated insulin secretion via GP130. Nat Metab, 2022, 4: 608-626.\u003c/li\u003e\n\u003cli\u003eTalukdar Saswata,Kharitonenkov Alexei. FGF19 and FGF21: In NASH we trust. Mol Metab, 2021, 46: 101152.\u003c/li\u003e\n\u003cli\u003eMisrahi Micheline. \u0026beta;-Klotho sustains postnatal GnRH biology and spins the thread of puberty. EMBO Mol Med, 2017, 9: 1334-1337.\u003c/li\u003e\n\u003cli\u003eSchumann Gunter,Liu Chunyu,O\u0026apos;Reilly Paul et al. KLB is associated with alcohol drinking, and its gene product \u0026beta;-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci U S A, 2016, 113: 14372-14377.\u003c/li\u003e\n\u003cli\u003eVikova Veronika,Jourdan Michel,Robert Nicolas et al. Comprehensive characterization of the mutational landscape in multiple myeloma cell lines reveals potential drivers and pathways associated with tumor progression and drug resistance. Theranostics, 2019, 9: 540-553.\u003c/li\u003e\n\u003cli\u003eZhang Meng,Yang Heli,Fu Tao et al. Liquid biopsy: circulating tumor DNA monitors neoadjuvant chemotherapy response and prognosis in stage II/III gastric cance. Mol Oncol, 2023, 17: 1930-1942.\u003c/li\u003e\n\u003cli\u003eWong Banny S,Camilleri Michael,Carlson Paula J et al. A Klotho\u0026beta; variant mediates protein stability and associates with colon transit in irritable bowel syndrome with diarrhea. Gastroenterology, 2011, 140: 1934-1942.\u003c/li\u003e\n\u003cli\u003eDongiovanni Paola,Crudele Annalisa,Panera Nadia et al. \u0026beta;-Klotho gene variation is associated with liver damage in children with NAFL. J Hepatol, 2020, 72: 411-419.\u003c/li\u003e\n\u003cli\u003ePanera Nadia,Meroni Marica,Longo Miriam et al. The KLB rs17618244 gene variant is associated with fibrosing MAFLD by promoting hepatic stellate cell activation. EBioMedicine, 2021, 65: 103249.\u003c/li\u003e\n\u003cli\u003eMehdi Ali,Rabbani Shafaat A,Role of Methylation in Pro- and Anti-Cancer Immunity. Cancers (Basel), 2021 Feb 1;13(3):545.\u003c/li\u003e\n\u003cli\u003eWang Shirong,Zha Lin,Cui Xin et al. Epigenetic Regulation of Hepatic Lipid Metabolism by DNA Methylation. Adv Sci (Weinh), 2023, 10: e2206068. \u003c/li\u003e\n\u003cli\u003eBecht Etienne,Giraldo Nicolas A,Dieu-Nosjean Marie-Caroline et al. Cancer immune contexture and immunotherapy. Curr Opin Immunol, 2016, 39: 7-13.\u003c/li\u003e\n\u003cli\u003eMao X, Xu J, Wang W et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021 Oct 11;20(1):131.\u003c/li\u003e\n\u003cli\u003eYang Yanlan,Li Huamei,Zheng Donghui et al. Immune microenvironment heterogeneity reveals distinct subtypes in neuroblastoma: insights into prognosis and therapeutic targets. Aging (Albany NY), 2023, 15: 13345-13367.\u003c/li\u003e\n\u003cli\u003eWong Banny S,Camilleri Michael,Carlson Paula J et al. A Klotho\u0026beta; variant mediates protein stability and associates with colon transit in irritable bowel syndrome with diarrhea. Gastroenterology, 2011, 140: 1934-1942.\u003c/li\u003e\n\u003cli\u003eDongiovanni Paola,Crudele Annalisa,Panera Nadia et al. \u0026beta;-Klotho gene variation is associated with liver damage in children with NAFLD. J Hepatol, 2020, 72: 411-419.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"KLB, colorectal cancer, pancancer analysis, prognosis, immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4085864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4085864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe incidence of colorectal cancer, a prevalent digestive system tumor, is increasing. Earlier research has demonstrated the significant impact of β-Klotho(KLB) on the development of metabolic disorders. Nonetheless, the function of KLB in tumors, particularly in colorectal cancer(CRC), remains underresearched.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBy employing databases such as the TCGA, GTEx, Human Protein Atlas, UALCAN, and cBioPortal, we gathered information regarding KLB expression levels, its predictive and diagnostic importance, epigenetic characteristics, various immune and molecular subtypes, immune checkpoints, and the extent of immune infiltration. The \u0026ldquo;clusterProfiler\u0026rdquo; R package was utilized for enrichment analysis to investigate the possible role of KLB. To determine the optimal prognostic model, multivariate Cox regression and Akaike's information criterion were applied. Additionally, CCK-8 assays, colony formation assays, and cell scratch assays were employed to assess the impact of KLB on the biological activities of CRC cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePancancer studies revealed a decrease in KLB in CRC and various other cancers, but an increase in KLB in liver hepatocellular carcinoma and prostate adenocarcinoma. Consequently, reduced KLB expression correlated with a lower TNM stage and unfavorable clinical outcomes in CRC patients. The nomogram, developed considering KLB, CEA level, and TNM stage, demonstrated enhanced predictive accuracy in CRC. Analysis of immune cell infiltration revealed a correlation between reduced KLB expression and decreased infiltration of immune cells. Experiments involving CCK-8, colony formation, and cell scratch assays revealed that the increased in vitro expression of KLB suppressed the growth, movement, and infiltration of CRC cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe expression levels of KLB were lower in CRC tissues than in normal tissues. A notable correlation was found between its reduced expression and a grim outlook. Furthermore, KLB is crucial for the immune response of tumors and the biological actions of CRC cells. Consequently, KLB could be a potential biomarker for prognosis and a target for therapy in CRC patients.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis reveals KLB as a prognostic biomarker in colorectal cancer based on systematic pancancer analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 20:24:47","doi":"10.21203/rs.3.rs-4085864/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f67fe14d-8c13-4057-9a0f-0a164eff4069","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-18T13:29:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-15 20:24:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4085864","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4085864","identity":"rs-4085864","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}