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

doi:10.21203/rs.3.rs-4085864/v1

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

https://doi.org/10.21203/rs.3.rs-4085864/v1

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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

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.

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.

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	130 (20.2%)	146 (22.7%)
> 65	192 (29.8%)	176 (27.3%)
BMI, n (%)			0.789
<= 25	47 (14.3%)	60 (18.2%)
> 25	101 (30.7%)	121 (36.8%)
CEA level, n (%)			0.685
<= 5	124 (29.9%)	137 (33%)
> 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
Characteristics	Total(N)	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	276	Reference
> 65	367	1.006 (0.737–1.371)	0.972
BMI	329		0.175
<= 25	107	Reference
> 25	222	1.381 (0.857–2.224)	0.185
CEA level	414		< 0.001
<= 5	260	Reference		Reference
> 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.

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.

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.

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.

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.

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No competing interests reported.

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

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Materials and methods

Gene expression analysis of KLB

Survival prognosis analysis

Genetic alteration analysis

Immune-related analysis

Klotho β-Related Gene Enrichment Analysis in Colorectal cancer

Prognostic Model Generation and Prediction

Tissue Specimens, Cell Lines, and Culture

RNA isolation and qRT‒PCR

Immunohistochemistry (IHC)

Cell Counting Kit-8 (CCK-8) analysis

Colony formation assay

Wound healing assay

Statistical analyses

RESULTS

KLB is differentially expressed in normal and tumor tissues

KLB Expression is Associated with the Prognosis of Various Tumors

Alterations in the KLB Gene Are Associated with the Development and Progression of Multiple Tumors

Correlations between KLB, tumor immune infiltration and immune checkpoint-related genes

Functional enrichment analysis of colon cancer samples with high and low KLB expression

Establishment of Prognostic Models for KLB In CRC Patients

KLB inhibits the proliferation and migration of colon cancer cells

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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