Gene expression profiling, prognosis, and immune microenvironment of KLF4 in malignancies

Gene expression profiling, prognosis, and immune microenvironment of KLF4 in malignancies | 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 Gene expression profiling, prognosis, and immune microenvironment of KLF4 in malignancies Shoukai Yu, Lingmei Qian, Liling Xu, Jun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4506656/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 Gene expression profiling plays a crucial role in understanding the role of Krüppel-like factor 4 (KLF4) in prognosis and the tumor immune microenvironment in various malignancies. The transcription factor KLF4 plays a crucial role in various cellular processes, including cell differentiation, proliferation, and apoptosis. Genetic alterations and aberrant KLF4 expression have been observed in many malignancies, thus suggesting a potential role as a prognostic marker and therapeutic target. Herein, a systematic analysis of KLF4 genetic alterations revealed the mutation, amplification, and deletion frequencies across different cancer types. The genetic alteration patterns varied across malignancies, thus highlighting the diverse roles of KLF4 in different tumor contexts. Secondly, the prognostic significance of KLF4 expression was assessed in multiple cancers. High expression levels of KLF4 were associated with better clinical outcomes in kidney renal clear cell carcinoma, while low KLF4 expression correlated with a favorable prognosis in certain malignancies. In conclusion, the genetic alterations, dysregulated RNA expression, and prognostic implications of KLF4 in malignancies underscore its significance in cancer biology. The findings present will aid in understanding the role of KLF4 in tumor biology and its association with immune responses. Further investigations should focus on further elucidating the functional roles and regulatory mechanisms of KLF4 to further assess its potential as a therapeutic target and predictive biomarker in cancer management. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Kruppel Like Factor 4 (KLF4) is a zinc finger protein that regulates diverse cellular processes and plays a key role in the induction of macrophage differentiation ( 1 ). KLF4 is also important in vascular health and aging, with KLF4 expression declining with age in vascular endothelium. Furthermore, aberrant KLF4 expression has been associated with various cancers, including cancer stem cells ( 2 ). KLF4, a zinc fingered transcription factor, has a crucial role in regulating cell proliferation, differentiation, and stem cell self-renewal. It is part of a group of four factors (Klf4, Oct4, Sox2, and c-Myc) that can stimulate the production of pluripotent stem cells ( 2 ). Additionally, KLF4 contributes to cell fate reprogramming and stem cell self-renewal and is considered essential to maintaining cancer stem cells ( 4 ). In various cancers, KLF4 has been shown to inhibit cell growth ( 3 ). Interestingly, in many tumors, KLF4 expression is often reduced or lost, which is associated with poor tumor differentiation. Moreover, this reduction in KLF4 expression in cancer cells is believed to be caused by genetic and epigenetic alterations ( 4 ). While KLF4 has been implicated in many cancers, the significance of alterations in KLF4 expression across a broad range of tumors requires further examination. In this study, a pan-cancer analysis was conducted to investigate the potential impact of KLF4 aberration in human malignant tumors. To our knowledge, this is the first pan-cancer analysis of KLF4 across different cancers. Herein, several databases and bioinformatics analyses were performed to elucidate alterations in KLF4 expression, variant diversities and clinical significance. Moreover, this study examined gene alterations and mutations, protein expression, survival prognosis, gene pathway enrichment, and immune infiltration. It is hoped that the findings presented herein will provide further clarity into the potential role of KLF4 as a prognostic marker and its molecular mechanism in human cancers. Methods Examination of differential KLF4 expression Initially, to investigate differential KLF4 mRNA expression between tumor and normal tissue, RNA-sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) project were evaluated using the tumor immune estimation resource version 2 (TIMER2, http://timer.cistrome.org/ ). For TCGA datasets with no normal samples or very limited normal samples, corresponding normal tissues from the Genotype-Tissue Expression (GTEx) database were utilized and the TCGA and GTEx datasets were evaluated using Gene Expression Profiling Interactive Analysis 2.0 (GEPIA2) ( 5 ). GEPIA2 was also used to investigate KLF4 expression at different pathological stages across the examined cancer types using the “Pathological Stage Plot” module ( http://gepia2.cancer-pku.cn/ ). To further investigate potential associations between tumor and normal tissues, KLF4 total protein expression datasets obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were examined with UALCAN ( 5 ). UALCAN is an interactive web resource that can be used to explore cancer OMICS data, including TCGA and CPTAC datasets. The cancer datasets that contained KLF4 expressional data included breast cancer, uterine corpus endometrial carcinoma (UCEC), pancreatic cancer, head and neck squamous carcinoma, and lung adenocarcinomas. Survival prognosis of KLF4 Survival analyses were conducted using GEPIA2 to generate overall survival (OS) and disease-specific survival (DSS) plots using the TCGA datasets, with the expression threshold cutoff set to 50% to separate high- and low-expression cohorts. For hypothesis testing, log-rank tests were applied. Within the “survival analysis” module in GEPIA2, OS and DSS associated p -values, q -values, and Kaplan-Meier plots were acquired. All statistical analyses were performed with the ‘survival’ package in R software. DNA methylation and genetic alterations analyses To evaluate KLF4 epigenetic regulation, DNA methylation levels were investigated using UALCAN and based on obtained TCGA datasets with two transcription start sites (TSS200 and TSS1500), with Illumina IDs cg03267342 and cg13894301 ( 6 ). Further genetic alterations, including mutations and copy number alterations (CNAs), were examined using the cBio Cancer Genomics Portal ( http://cbioportal.org ). The results include the mutations, amplifications, profound deletions of KLF4 and three-dimensional (3D) structure of KLF4 mutations. Immune infiltration analysis of KLF4 To investigate potential associations between KLF4 expression and immune cell, CD8 + T cells, and cancer-associated fibroblast infiltration, the “Immune-Gene” module within the TIMER2 web server was employed. Immune infiltration estimates were determined using ten algorithms (TIMER, EPIC, MCP-COUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, and four XCELL related algorithms), with purity-adjusted Spearman’s rank analysis performed and associated p -values and correlation values determined. Immune microenvironment analysis Pearson's correlation between genes and immunoinfiltration scores were calculated using the corr.test function in the R package_psych, with the immunoinfiltration scores derived from 10,180 tumor samples from 44 tumor types. The corresponding clinical features were also downloaded from the University of California Santa Cruz (UCSC, https://xenabrowser.net/ ) database. Next, the correlation expression data for the ENSG00000136826 (KLF4) gene and 44 marker genes belonging to three classes of RNA modification (m1A, m5C, m6A) were extracted. The sample sources were as follows: primary solid tumor, primary tumor, primary blood derived cancer-bone marrow, and primary peripheral blood-derived cancer All normal samples were filtered and log2(x + 0.001) transformed. Next, we calculated the Pearson correlation between ENSG00000136826 (KLF4) and five classes of immune pathways. To evaluate any significant associations between KLF4 expression and the infiltration of 28 distinct immune cell subtypes, Sangerbox platform ( http://sangerbox.com/ ) was employed. The ESTIMATE algorithm ( 7 ) was employed to evaluate immune cell infiltration and tumor purity. This algorithm utilizes the specific signatures that are associated with immune and stromal infiltration and is based on single sample gene set enrichment analysis (ssGSEA). These scores then form the basis for the ESTIMATE score, which reflects tumor purity. Expression for the KLF4 gene and 60 genes belonging to two types of immune checkpoint pathways, to include associated markers, were extracted. The strength of the correlation is represented by color, while asterisks denote statistically significant p-values obtained through spearman correlation analysis (*P < 0.05; **P < 0.01; ***P < 0.001). The DNA methylation-based stemness score (DNAss) was calculated based on tumor methylation characteristics and based on TCGA datasets. We integrated the sample stemness index and gene expression data, and further transformed each expression value by log2(x + 0.001). Finally, tumor cohorts with less than 3 samples were eliminated. Additionally, DNAss, RNA stemness score (RNAss), and RNA-Seq data, along with clinical data, were downloaded from the UCSC Xena database. The DNAss, which represents the DNAss of cuproptosis-associated genes, was assessed using the R packages "limma" and "corrplot," with Spearman's analysis also employed. Tumor epigenetic features were evaluated based on the DNAss, while gene expression was evaluated based on the RNAss. The scores ranged from 0 to 1, with 0 indicating high differentiation and 1 indicating undifferentiated. Subsequently, the DNAss and RNAss were analyzed among the given tumor cohort subgroups. Gene-related enrichment analysis To identify the 50 top potential KLF4-binding proteins for further analyses, the STRING database ( https://string-db.org/ ) was employed. Next, GEPIA2 was employed to identify the 100 top genes with an expression pattern similar to that of KLF4 in all TCGA tumor and normal samples. Pearson correlations were performed and associated p -values and correlation coefficients were determined A cnet plot was generated using TIMER2, to include p -values and partial correlations in the purity-adjusted Spearman's test. A Venn diagram was employed to conduct an intersectional analysis of KLF4 binding and interacting genes base on STING and GEPIA2 findings. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were performed using the "clusterProfiler" package in R statistical software version 4.0.5, and bubble plots were created using the "tidyr" and "ggplot2" packages. Results Gene expression analysis Initially, KLF4 expression was examined across different cancer types using TCGA datasets with the TIMER2 server. The results showed that KLF4 expression is significantly downregulated in BLCA, BRCA, COAD, HNSC, KICH, LIHC, LUAD, LUSC, READ, STAD, THCA, and UCEC tumor tissues relative to their corresponding normal tissues (Fig. 1 A, p < 0.001). When combining the TCGA and GTEx datasets to compensate for cancers with little to no normal samples, BLCA, BRCA, LUAD, OV, SKCM, UCEC, and UCS were identified as having significantly decreased KLF4 expression using GEPIA2 (Fig. 1 B, p < 0.001); while LAML and PAAD were significantly increased relative to their corresponding normal tissues (Fig. 1 C, p < 0.001). To determine if KLF4 expression differs based on tumor pathological stage, the “Pathological Stage Plot” module of GEPIA2 was employed. The results demonstrated that KLF4 expression is significantly associated with clinical stage in six of the examined cancer types: BRCA ( p = 0.0384), KIRC ( p = 2.68e-07), KIRP ( p = 1.02e-06), PAAD ( p = 0.0126), TGCT ( p = 0.0224) and THCA ( p = 0.0393) (Figure S1). Furthermore, the results of the CPTAC dataset showed lower KLF4 total protein expression in the primary tissues of HNSC and LAUD (Figure S2, p < 0.05) relative to the normal controls; while higher KLF4 expression was noted in PAAD primary tissues ( p < 0.05) relative to the normal controls. Analysis of survival prognosis To determine the prognostic potential of KLF4, OS and DSS plots were generated. The results of OS showed that highly expressed KLF4 is linked with a poor overall prognosis in THYM ( p = 0.04), and UVM (Fig. 2 , p = 0.003); while low KLF4 expression is significantly correlated with a poor prognosis in KIRC ( p < 0.001). Interestingly, the results also showed that high KLF4 expression is significantly correlated with a poor prognosis in the early stages in LUAD ( p = .011). Additionally, DSS analysis (Figure S3) showed that high KLF4 expression is significantly correlated with a poor prognosis in LUAD ( p = 0.008) and UVM ( p = 0.003), while a low KLF4 expression is significantly correlated with a poor prognosis in KIRC ( p < 0.001) and KIRP ( p = 0.008). Both the OS and DSS plots showed that there are some direct association between KLF4 expression levels and prognosis. DNA methylation and genetic alteration analysis To investigate potential methylation alterations at the KLF4 promoter, UALCAN was utilized (Figure S4). Interestingly, respiratory system-related tumors, such as LUAD and LUSC, exhibited an increase in DNA methylation. For the tumors with upregulated KLF4 (LAML and PAAD), the DNA methylation level could not be predicted based on the mRNA expression features. Additionally, due to the unavailability of a DNA methylation dataset for KICH and UVM normal controls, comparative analysis was conducted across different patient populations. These findings confirm that aberrant KLF4 expression is not solely attributed to DNA methylation. Thus, further investigations could be conducted to explore histone modifications ( 5 ) and glycosylation ( 6 ). Genetic alterations in KLF4 were examined using the cBio portal, with 10,953 samples out of 10,967 samples from the TCGA database for different cancers examined. The pan-cancer analysis of KLF4 revealed that the highest alteration frequencies are present in UCEC (3.4%) and BLCA (2.92%) (Fig. 3 A). The highest frequencies of mutations were predominantly found in UCEC, DLBC and PAAD. Amplifications comprised the majority of alterations in ACC and SARC. Moreover, the highest frequencies of deep deletions were observed in BLCA and THCA (Fig. 3 A). The most frequently observed mutation was K409Q/N, and a 3D structure of this KLF4 mutation was constructed (Fig. 3 B). Further analysis revealed that the KLF4 mutations in various malignancies are distributed throughout without the presence of a mutational hotspot site (Fig. 3 C). KLF4 alterations in each given tumor type occur at a very low frequency (Figure S5). Additionally, copy number variations were also found to not be significantly associated with KLF4 expression across all tumors (Figure S6). One possible explanation is that aberrant KLF4 expression is not directly influenced by genetic alterations. Analysis of total protein expression To compare KLF4 total protein levels between normal and primary tumor tissues, CPTAC datasets were utilized for three types of tumors (HNSC, LUAD, and PAAD). The results showed significantly lower protein levels in primary tumor tissues compared to normal tissues for HNSC and LUAD (Figure S2, p < 0.05). Higher KLF4 expression was noted in the primary PAAD tissues ( p < 0.05) relative to the normal controls. Immune infiltration analysis Tumor infiltrating immune cells have been recognized as an important component of the tumor microenvironment and play a crucial role in tumor initiation, promotion, progression, and metastasis ( 8 ). Moreover, cancer-associated fibroblasts have also been implicated in the modulation of various tumor infiltrating immune cells ( 10 ). Thus, KLF4 expression and immune cell infiltration levels were examined across various TCGA tumor types using TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms. The various analyses indicated that KLF4 expression is positively correlated with CD8 + T cell infiltration in HNSC-HPV- and UVM, while negatively correlated in LUSC and THYM, based on most of the algorithms (Figure S7). Additionally, KLF4 expression and cancer-associated fibroblast infiltration was found to be positively correlated in BRCA, BRCA-LumA, LIHC, and PCPG, while negatively correlated with HNSC, HNSC-HPV-, STAD and TGCT, based on all or most algorithms (Fig. 4 A). These findings were then confirmed for a subset of the tumors using scatter plots (Fig. 4 B–D). Moreover, KLF4 clinical significance was demonstrated in BLCA, BRCA, CESC, LAML, PAAD, PCPG, SKCM, THCA, UCEC, and UCS via ROC analysis (Figure S8). Immune microenvironment To evaluate the overall presence of immune cells, the ESTIMATE algorithm was employed. Our analysis revealed a notable decrease in ESTIMATE scores in metastatic tumors when compared to primary tumors (Figure S9), thus indicating a diminished immune cell infiltration in metastatic lesions. Furthermore, KLF4 expression was significantly correlated with immunoinfiltration in 32 cancer species, with 6 being significantly negatively correlated: TCGA-CESC ( N = 291, r = − 0.15, p = 9.6e-3), TCGA-ESCA ( N = 181, r = − 0.20, p = 7.0e-3), TCGA-STES ( N = 569, r = − 0.19, p = 3.3e-6), TCGA-HNSC ( N = 517, r = − 0.20, p = 7.5e-6), TCGA-LUSC ( N = 491, r = − 0.13, p = 3.6e-3), TCGA-TGCT ( N = 132, r = − 0.34, p = 6.3e-5). RNA modifications can directly affect the chemical properties of the molecule, including its charge, base pairing, secondary structure, and protein-RNA interactions. These modifications can in turn regulate gene expression by controlling RNA processing, localization, translation, and eventual decay ( 9 , 10 ). Herein, m6A (N6-methyladenosine), m1A (N1-methyladenosine), and m5C (5-methylcytosine) were the most common RNA methylation modifications. Pearson’s correlation analysis showed that m6A methylation plays a biological role mainly through RNA-binding proteins (Figure S10). Furthermore, when examining correlations between tumor KLF4 expression and immune checkpoint genes as a means to predict immunotherapy applications, a negative correlation between KLF4 and a wide range of immunomodulators was noted (Figure S11). Overall, the top four checkpoint genes associated with KLF4 and correlated with a tumor inhibitory effect in several tumors were C10orf54, CD274, IL10, and TGFB1. While the top three KLF4-assocaited genes correlated with a tumor stimulatory effect for several tumors were CX3CL1, TNFRSF4, and IL1A. Meanwhile, the top three genes which are least correlated with KLF4 and associated with a tumor inhibitory effect in several tumors were BTLA, IDO1, and LAG3. The KLF4-assocaited genes least correlated with a tumor stimulatory effect for several tumors including TNFRSF18. Cells that possess stem-cell like characteristics, stemness, have a higher tendency toward tumorigenic behavior, to include DNA hypermethylation of specific promoters to suppress gene expression ( 11 ). Thus, to discern potential correlations between KLF4 expression and DNAss, Pearson’s correlations were performed based on immune checkpoint genes and KLF4 expression using Sangerbox ( p < 0.05). The results identified 14 significant associations, with MESO and PCPG being the most negatively correlated and THYM and TGCT being the most positively correlated (Figure S12). The correlation between KLF4 expression and cancer stemness scores (DNAss) based on the Pearson’s correlation analysis is shown in Figure S12, with only a few associations achieving statistical significance. Enrichment of KLF4-related partners To further explore the molecular mechanism of KLF4 in tumorigenesis, KLF4 binding and potential correlated expression was examined using several pathway enrichment analyses. The STRING tool identified a total of 50 experimentally supported binding proteins and an interaction network was constructed (Fig. 5 ). To further expand the list, GEPIA2 was employed and the top 100 genes associated with KLF4 expression were determined using all TCGA tumor expression data (data not shown). These STRING and GEPIA2 datasets were then combined and further enrichment analysis was performed using KEGG and GO enrichment analyses. The KEGG analysis identified “Cell cycle” pathways as associated with KLF4 (Fig. 5 B), while GO analysis identified biological processes such as "epidermis-development", "skin-development", and "epidermal-cell-differentiation" (Fig. 5 D). Furthermore, an intersection analysis of the STING and GEPIA2 datasets identified one common member (Fig. 5 C). Notably, the KEGG pathways encompassed "cell cycle," while the GO analysis identified "epidermis development," "skin development," and "epidermal cell differentiation" in association with the biological processes category Discussion Previous studies have demonstrated that KLF4 exhibits a cell type-dependent role in either tumor suppression or promotion ( 12 ). Additionally, KLF4 has been identified as a prognostic predictor for urothelial carcinoma of the bladder, where it regulates TWIST1-mediated epithelial-mesenchymal transition ( 12 ). However, the precise role of the multifunctional KLF4 in the molecular pathogenesis of various tumors remains unclear. Therefore, this study employed a pan-caner approach to identify alterations in KLF4 DNA sequence, gene and protein expression, and DNA methylation in over 30 tumors. Notably, a significant downregulation of KLF4 expression was observed in BLCA, BRCA, LUAD, OV, SKCM, UCEC, and UCS. To our knowledge, this is the first pan-cancer study to characterize KLF4 genetic and epigenetic variations in various human malignant tumors. To identify differential KLF4 total protein expression between various primary and tumor samples the CPTAC database was employed (Figure S2). KLF4 was upregulated in PAAD relative to the normal control, while it was significantly downregulated in HNSC and LUAD. Furthermore, DSS analysis revealed that low KLF4 expression is associated with a poor prognosis for KIRP and KIRC. However, in LUAD and UVM, high levels of KLF4 expression were associated with an unfavorable prognosis. Overall, this comprehensive investigation of KLF4 across multiple cancer types reveals its substantial clinical significance in terms of prognosis, protein expression, and immune cell infiltration in some cancers. Moreover, KLF4 appears to exert a crucial role in both cancer cell stemness and tumor development. Since KLF4 has been implicated as a potential immune-related molecule, it might serve as a potential prognostic marker. These findings indicate that further examination into the role of KLF4 in tumorigenesis is warranted to gain a deeper understanding into its role in the tumor microenvironment. In a previous study examining colorectal cancer, downregulated KLF4 expression was associated with a poor prognosis ( 13 ). Herein, a similar association was observed in COAD and READ, but the association did not achieve statistical significance following OS/DSS analysis when combining the cohorts (Figure S12). Furthermore, while immune checkpoint upregulation is commonly linked to a poor prognosis, these tumors are more likely to demonstrate a favorable response to immune checkpoint inhibitors. The findings indicated that KLF4 should be considered a target of interest. Thus further characterizing the role of KLF4 will enhance our comprehension of the regulatory pathways governing immune checkpoint modulation and potentially enhance the success rate and effectiveness of immune checkpoint therapy (ICT). Furthermore, this study reinforces the results of previous meta-analyses ( 14 ), which found that KLF4 expression is not directly associated with the prognosis of all types of tumors. In other words, KLF4 operates through different mechanisms in different tumor types. To further examine the mechanisms of KLF4, immune checkpoint genes were examined in association with KLF4 expression. Herein, several KLF4-assocaited genes, C10orf54 and CD274 were correlated with tumor suppression; while others, CX3CL1, TNFRSF4, and IL1A, were correlated with tumor stimulation. In previous studies, BTLA, LAG3, and IDO1 have been identified as immune checkpoint inhibitors ( 15 – 18 ). KLF4 plays an important role in tumorigenesis via immune regulation and promotes the cytolytic effector function in a subset of CD8 T cells ( 15 ). KLF4 has also been shown to function in the re-activation of CD8 T cells in the immune response against cancer ( 16 ). Collectively, the results suggest the higher expression of KLF4 indicates the positive correlation with cellular markers of immune activation. These results highlight the potential value of KLF4 as a clinical biomarker and a specific therapeutic target. The results of the current study show that KLF4 is associated with cancer prognosis and immune infiltration across several different types of cancers, which is consistent with a recent study ( 17 ). Moreover, KLF4 expression is also strongly associated with immune-related gene expression in various cancers and may play a crucial role as a prognostic biomarker for different cancers and possible therapeutic target across several different cancer types. Declarations Ethical Approval Not applicable Competing interests Authors declare no conflict of interests. Funding Not applicable Author Contribution Shoukai Yu: Conceptualization, Methodology, Software Lingmei Qian and Liling Xu: Data curation, Writing- Original draft preparation. Jun Ma: Visualization, Investigation, Supervision. Shoukai Yu: Software, Validation.: All authors: Writing- Reviewing and Editing. Availability of data and materials All of data in this study are publicly available as described. References Shi M, Cui J, Du J, Wei D, Jia Z, Zhang J, et al. A novel KLF4/LDHA signaling pathway regulates aerobic glycolysis in and progression of pancreatic cancer. Clin cancer research: official J Am Association Cancer Res. 2014;20(16):4370–80. Moon BS, Bai J, Cai M, Liu C, Shi J, Lu W. Kruppel-like factor 4-dependent Staufen1-mediated mRNA decay regulates cortical neurogenesis. Nat Commun. 2018;9(1):401. Yu T, Chen X, Zhang W, Liu J, Avdiushko R, Napier DL, et al. 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The Role of Immune Escape and Immune Cell Infiltration in Breast Cancer. Breast care (Basel. Switzerland). 2018;13(1):16–21. Zaccara S, Ries RJ, Jaffrey SR. Reading, writing and erasing mRNA methylation. Nat Rev Mol Cell Biol. 2019;20(10):608–24. Sun H, Li K, Liu C, Yi C. Regulation and functions of non-m(6)A mRNA modifications. Nat Rev Mol Cell Biol. 2023;24(10):714–31. Ohm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007;39(2):237–42. Tseng WC, Chuang CW, Yang MH, Pan CC, Tarng DC. Krüppel-like factor 4 is a novel prognostic predictor for urothelial carcinoma of bladder and it regulates TWIST1-mediated epithelial-mesenchymal transition. Urol Oncol. 2016;34(11):485. .e15-.e24 . Sajadi M, Fazilti M, Nazem H, Mahdevar M, Ghaedi K. The expression changes of transcription factors including ANKZF1, LEF1, CASZ1, and ATOH1 as a predictor of survival rate in colorectal cancer: a large-scale analysis. Cancer Cell Int. 2022;22(1):339. Luo X, Zhang Y, Meng Y, Ji M, Wang Y. Prognostic significance of KLF4 in solid tumours: an updated meta-analysis. BMC Cancer. 2022;22(1):181. Arora S, Singh P, Ahmad S, Ahmad T, Dohare R, Almatroodi SA et al. Comprehensive Integrative Analysis Reveals the Association of KLF4 with Macrophage Infiltration and Polarization in Lung Cancer Microenvironment. Cells [Internet]. 2021; 10(8). Nah J, Seong RH. Krüppel-like factor 4 regulates the cytolytic effector function of exhausted CD8 T cells. Sci Adv.8(47):eadc9346. He Z, He J, Xie K. KLF4 transcription factor in tumorigenesis. Cell death discovery. 2023;9(1):118. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4506656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310692878,"identity":"eb154f1f-46b5-467f-b3ab-48caa25c05a7","order_by":0,"name":"Shoukai Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACCQYGxgYQyd7AYAAWOUC0Fp4DDAYHSNACYiRAVRPSIj+7+ZjkjBqLxA03nz8o/tjGIMd3I4HxcwEeLQZ3jqVJbjgmkbjhdo6BwcE2BmPJGwnM0jPwaZHIMZN8wAbWwgDSkrjhRgIbMw8+h80AafkH1HLz+AOQlnqCWhhuALVsbANqucEAdliCASEtBjfSki1n9kkYzzwD9MuZcxKGM888bJbG77Dkgzd7vtXJ9h0//sygosxGnu948sHPeB0GBY4NDAxsBuBogsYTQWAPxMwPiFI6CkbBKBgFIw4AAFC4UqkBBWhIAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Shoukai","middleName":"","lastName":"Yu","suffix":""},{"id":310692879,"identity":"35e25246-04b0-4d82-9242-aa8dfb0df416","order_by":1,"name":"Lingmei Qian","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Lingmei","middleName":"","lastName":"Qian","suffix":""},{"id":310692880,"identity":"913197a7-1cb9-40cd-ab59-d54d4a326b21","order_by":2,"name":"Liling Xu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Liling","middleName":"","lastName":"Xu","suffix":""},{"id":310692881,"identity":"e1194325-d557-4e5d-b405-ffc1429d163a","order_by":3,"name":"Jun Ma","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-05-31 06:26:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4506656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4506656/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58583765,"identity":"c46c0bb4-e605-4713-83f4-2e9a771620a0","added_by":"auto","created_at":"2024-06-18 13:46:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363026,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of KLF4 RNA expression in various tumors utilizing TCGA and GTEx datasets. (A) Assessment of KLF4 expression in different cancer types using TIMER2. Statistical significance is denoted as *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. (B) and (C) TCGA and GTEx datasets were examined using GEPIA2, with corresponding normal tissues from the GTEx database utilized as control samples for TCGA cancers with little or no control samples. Statistical significance is denoted as *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. In the TCGA dataset, expression levels were significantly lower in the cancer groups relative to the normal tissues; while in the GTEx dataset, the opposite was noted.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/56d9f0311fb028414abe97a8.png"},{"id":58583767,"identity":"8cdcd98e-fbe8-4cd1-86cb-9a8dc61b15f8","added_by":"auto","created_at":"2024-06-18 13:46:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83887,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival outcomes analysis in malignancies displaying abnormal KLF4 expression.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/b2ab7de000d5a056e8b82759.png"},{"id":58584481,"identity":"a75adbbf-df38-45a1-a0ac-c54772f057fd","added_by":"auto","created_at":"2024-06-18 13:54:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":301317,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of KLF4 alterations using the cBio portal. (A) Analysis of genetic variations in KLF4 across different tumor types. (B) Visualization of the three-dimensional structure of KLF4 mutations, with the mutation R163*/Q highlighted in yellow. (C) Distribution of KLF4 mutations across all exons without a prominent hotspot mutation site being noted in the TCGA cohort.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/dcfb0ddc0b83a50323a9fcf4.png"},{"id":58583766,"identity":"4cc97137-2096-4307-96d6-937870462ddb","added_by":"auto","created_at":"2024-06-18 13:46:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464859,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the relationship between KLF4 expression and cancer-associated fibroblast (CAF) infiltration. (A) Four computational algorithms (EPIC, MCPCOUNTER, XCELL, and TIDE) were employed to investigate the possible link between KLF4 expression and CAF infiltration in various cancer types. Positive correlations were then confirmed in (B) BRCA, (C) LIHC and (D) PCPG via scatter plots.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/a3c90a696b4d2b2992842402.png"},{"id":58583769,"identity":"cbfcd463-2295-4fe1-8f0a-4d282b8347f0","added_by":"auto","created_at":"2024-06-18 13:46:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210476,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of the KLF4 gene. (A) Utilizing the STRING tool, fifty proteins were identified that interact with KLF4. (B) KEGG pathway analysis was performed based on the genes identified from the STRING and GEPIUA2 analyses that interact with KLF4. (C) An intersection analysis was performed to identify genes that both bind to and are correlated with KLF4. (D) GO analysis was performed and the results visualized by constructing a cnet plot.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/82735dae2afc53cbe05aa523.png"},{"id":74952226,"identity":"2a8b29d6-1e7f-4077-bc45-4f6b1f404284","added_by":"auto","created_at":"2025-01-28 16:33:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1956416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/41cf13d2-c7ab-4523-995b-5bdc1e5c720f.pdf"},{"id":58584482,"identity":"e07a077e-c916-4744-b191-1d89cd8c9ea6","added_by":"auto","created_at":"2024-06-18 13:54:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4332684,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4506656/v1/57347a7adc9a3fafed65e3a2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gene expression profiling, prognosis, and immune microenvironment of KLF4 in malignancies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKruppel Like Factor 4 (KLF4) is a zinc finger protein that regulates diverse cellular processes and plays a key role in the induction of macrophage differentiation (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). KLF4 is also important in vascular health and aging, with KLF4 expression declining with age in vascular endothelium. Furthermore, aberrant KLF4 expression has been associated with various cancers, including cancer stem cells (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKLF4, a zinc fingered transcription factor, has a crucial role in regulating cell proliferation, differentiation, and stem cell self-renewal. It is part of a group of four factors (Klf4, Oct4, Sox2, and c-Myc) that can stimulate the production of pluripotent stem cells (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Additionally, KLF4 contributes to cell fate reprogramming and stem cell self-renewal and is considered essential to maintaining cancer stem cells (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In various cancers, KLF4 has been shown to inhibit cell growth (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Interestingly, in many tumors, KLF4 expression is often reduced or lost, which is associated with poor tumor differentiation. Moreover, this reduction in KLF4 expression in cancer cells is believed to be caused by genetic and epigenetic alterations (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While KLF4 has been implicated in many cancers, the significance of alterations in KLF4 expression across a broad range of tumors requires further examination.\u003c/p\u003e \u003cp\u003eIn this study, a pan-cancer analysis was conducted to investigate the potential impact of KLF4 aberration in human malignant tumors. To our knowledge, this is the first pan-cancer analysis of KLF4 across different cancers. Herein, several databases and bioinformatics analyses were performed to elucidate alterations in KLF4 expression, variant diversities and clinical significance. Moreover, this study examined gene alterations and mutations, protein expression, survival prognosis, gene pathway enrichment, and immune infiltration. It is hoped that the findings presented herein will provide further clarity into the potential role of KLF4 as a prognostic marker and its molecular mechanism in human cancers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExamination of differential KLF4 expression\u003c/h2\u003e \u003cp\u003eInitially, to investigate differential KLF4 mRNA expression between tumor and normal tissue, RNA-sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) project were evaluated using the tumor immune estimation resource version 2 (TIMER2, \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). For TCGA datasets with no normal samples or very limited normal samples, corresponding normal tissues from the Genotype-Tissue Expression (GTEx) database were utilized and the TCGA and GTEx datasets were evaluated using Gene Expression Profiling Interactive Analysis 2.0 (GEPIA2) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). GEPIA2 was also used to investigate KLF4 expression at different pathological stages across the examined cancer types using the \u0026ldquo;Pathological Stage Plot\u0026rdquo; module (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further investigate potential associations between tumor and normal tissues, KLF4 total protein expression datasets obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were examined with UALCAN (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). UALCAN is an interactive web resource that can be used to explore cancer OMICS data, including TCGA and CPTAC datasets. The cancer datasets that contained KLF4 expressional data included breast cancer, uterine corpus endometrial carcinoma (UCEC), pancreatic cancer, head and neck squamous carcinoma, and lung adenocarcinomas.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival prognosis of KLF4\u003c/h3\u003e\n\u003cp\u003eSurvival analyses were conducted using GEPIA2 to generate overall survival (OS) and disease-specific survival (DSS) plots using the TCGA datasets, with the expression threshold cutoff set to 50% to separate high- and low-expression cohorts. For hypothesis testing, log-rank tests were applied. Within the \u0026ldquo;survival analysis\u0026rdquo; module in GEPIA2, OS and DSS associated \u003cem\u003ep\u003c/em\u003e-values, \u003cem\u003eq\u003c/em\u003e-values, and Kaplan-Meier plots were acquired. All statistical analyses were performed with the \u0026lsquo;survival\u0026rsquo; package in R software.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA methylation and genetic alterations analyses\u003c/h2\u003e \u003cp\u003eTo evaluate KLF4 epigenetic regulation, DNA methylation levels were investigated using UALCAN and based on obtained TCGA datasets with two transcription start sites (TSS200 and TSS1500), with Illumina IDs cg03267342 and cg13894301 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Further genetic alterations, including mutations and copy number alterations (CNAs), were examined using the cBio Cancer Genomics Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cbioportal.org\u003c/span\u003e\u003cspan address=\"http://cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The results include the mutations, amplifications, profound deletions of KLF4 and three-dimensional (3D) structure of KLF4 mutations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis of KLF4\u003c/h2\u003e \u003cp\u003eTo investigate potential associations between KLF4 expression and immune cell, CD8\u0026thinsp;+\u0026thinsp;T cells, and cancer-associated fibroblast infiltration, the \u0026ldquo;Immune-Gene\u0026rdquo; module within the TIMER2 web server was employed. Immune infiltration estimates were determined using ten algorithms (TIMER, EPIC, MCP-COUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, and four XCELL related algorithms), with purity-adjusted Spearman\u0026rsquo;s rank analysis performed and associated \u003cem\u003ep\u003c/em\u003e-values and correlation values determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmune microenvironment analysis\u003c/h2\u003e \u003cp\u003ePearson's correlation between genes and immunoinfiltration scores were calculated using the corr.test function in the R package_psych, with the immunoinfiltration scores derived from 10,180 tumor samples from 44 tumor types. The corresponding clinical features were also downloaded from the University of California Santa Cruz (UCSC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. Next, the correlation expression data for the ENSG00000136826 (KLF4) gene and 44 marker genes belonging to three classes of RNA modification (m1A, m5C, m6A) were extracted. The sample sources were as follows: primary solid tumor, primary tumor, primary blood derived cancer-bone marrow, and primary peripheral blood-derived cancer All normal samples were filtered and log2(x\u0026thinsp;+\u0026thinsp;0.001) transformed. Next, we calculated the Pearson correlation between ENSG00000136826 (KLF4) and five classes of immune pathways.\u003c/p\u003e \u003cp\u003eTo evaluate any significant associations between KLF4 expression and the infiltration of 28 distinct immune cell subtypes, Sangerbox platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed. The ESTIMATE algorithm (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) was employed to evaluate immune cell infiltration and tumor purity. This algorithm utilizes the specific signatures that are associated with immune and stromal infiltration and is based on single sample gene set enrichment analysis (ssGSEA). These scores then form the basis for the ESTIMATE score, which reflects tumor purity. Expression for the KLF4 gene and 60 genes belonging to two types of immune checkpoint pathways, to include associated markers, were extracted. The strength of the correlation is represented by color, while asterisks denote statistically significant p-values obtained through spearman correlation analysis (*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe DNA methylation-based stemness score (DNAss) was calculated based on tumor methylation characteristics and based on TCGA datasets. We integrated the sample stemness index and gene expression data, and further transformed each expression value by log2(x\u0026thinsp;+\u0026thinsp;0.001). Finally, tumor cohorts with less than 3 samples were eliminated. Additionally, DNAss, RNA stemness score (RNAss), and RNA-Seq data, along with clinical data, were downloaded from the UCSC Xena database. The DNAss, which represents the DNAss of cuproptosis-associated genes, was assessed using the R packages \"limma\" and \"corrplot,\" with Spearman's analysis also employed. Tumor epigenetic features were evaluated based on the DNAss, while gene expression was evaluated based on the RNAss. The scores ranged from 0 to 1, with 0 indicating high differentiation and 1 indicating undifferentiated. Subsequently, the DNAss and RNAss were analyzed among the given tumor cohort subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene-related enrichment analysis\u003c/h2\u003e \u003cp\u003eTo identify the 50 top potential KLF4-binding proteins for further analyses, the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed. Next, GEPIA2 was employed to identify the 100 top genes with an expression pattern similar to that of KLF4 in all TCGA tumor and normal samples. Pearson correlations were performed and associated \u003cem\u003ep\u003c/em\u003e-values and correlation coefficients were determined\u003c/p\u003e \u003cp\u003eA cnet plot was generated using TIMER2, to include \u003cem\u003ep\u003c/em\u003e-values and partial correlations in the purity-adjusted Spearman's test. A Venn diagram was employed to conduct an intersectional analysis of KLF4 binding and interacting genes base on STING and GEPIA2 findings. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses were performed using the \"clusterProfiler\" package in R statistical software version 4.0.5, and bubble plots were created using the \"tidyr\" and \"ggplot2\" packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGene expression analysis\u003c/h2\u003e \u003cp\u003eInitially, KLF4 expression was examined across different cancer types using TCGA datasets with the TIMER2 server. The results showed that KLF4 expression is significantly downregulated in BLCA, BRCA, COAD, HNSC, KICH, LIHC, LUAD, LUSC, READ, STAD, THCA, and UCEC tumor tissues relative to their corresponding normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When combining the TCGA and GTEx datasets to compensate for cancers with little to no normal samples, BLCA, BRCA, LUAD, OV, SKCM, UCEC, and UCS were identified as having significantly decreased KLF4 expression using GEPIA2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); while LAML and PAAD were significantly increased relative to their corresponding normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eTo determine if KLF4 expression differs based on tumor pathological stage, the \u0026ldquo;Pathological Stage Plot\u0026rdquo; module of GEPIA2 was employed. The results demonstrated that KLF4 expression is significantly associated with clinical stage in six of the examined cancer types: BRCA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0384), KIRC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.68e-07), KIRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.02e-06), PAAD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0126), TGCT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0224) and THCA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0393) (Figure S1). Furthermore, the results of the CPTAC dataset showed lower KLF4 total protein expression in the primary tissues of HNSC and LAUD (Figure S2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative to the normal controls; while higher KLF4 expression was noted in PAAD primary tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative to the normal controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of survival prognosis\u003c/h2\u003e \u003cp\u003eTo determine the prognostic potential of KLF4, OS and DSS plots were generated.\u003c/p\u003e \u003cp\u003eThe results of OS showed that highly expressed KLF4 is linked with a poor overall prognosis in THYM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), and UVM (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.003); while low KLF4 expression is significantly correlated with a poor prognosis in KIRC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, the results also showed that high KLF4 expression is significantly correlated with a poor prognosis in the early stages in LUAD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011). Additionally, DSS analysis (Figure S3) showed that high KLF4 expression is significantly correlated with a poor prognosis in LUAD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and UVM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), while a low KLF4 expression is significantly correlated with a poor prognosis in KIRC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and KIRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Both the OS and DSS plots showed that there are some direct association between KLF4 expression levels and prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA methylation and genetic alteration analysis\u003c/h2\u003e \u003cp\u003eTo investigate potential methylation alterations at the KLF4 promoter, UALCAN was utilized (Figure S4). Interestingly, respiratory system-related tumors, such as LUAD and LUSC, exhibited an increase in DNA methylation. For the tumors with upregulated KLF4 (LAML and PAAD), the DNA methylation level could not be predicted based on the mRNA expression features. Additionally, due to the unavailability of a DNA methylation dataset for KICH and UVM normal controls, comparative analysis was conducted across different patient populations. These findings confirm that aberrant KLF4 expression is not solely attributed to DNA methylation. Thus, further investigations could be conducted to explore histone modifications (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and glycosylation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic alterations in KLF4 were examined using the cBio portal, with 10,953 samples out of 10,967 samples from the TCGA database for different cancers examined. The pan-cancer analysis of KLF4 revealed that the highest alteration frequencies are present in UCEC (3.4%) and BLCA (2.92%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The highest frequencies of mutations were predominantly found in UCEC, DLBC and PAAD. Amplifications comprised the majority of alterations in ACC and SARC. Moreover, the highest frequencies of deep deletions were observed in BLCA and THCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most frequently observed mutation was K409Q/N, and a 3D structure of this KLF4 mutation was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Further analysis revealed that the KLF4 mutations in various malignancies are distributed throughout without the presence of a mutational hotspot site (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eKLF4 alterations in each given tumor type occur at a very low frequency (Figure S5). Additionally, copy number variations were also found to not be significantly associated with KLF4 expression across all tumors (Figure S6). One possible explanation is that aberrant KLF4 expression is not directly influenced by genetic alterations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of total protein expression\u003c/h2\u003e \u003cp\u003eTo compare KLF4 total protein levels between normal and primary tumor tissues, CPTAC datasets were utilized for three types of tumors (HNSC, LUAD, and PAAD). The results showed significantly lower protein levels in primary tumor tissues compared to normal tissues for HNSC and LUAD (Figure S2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Higher KLF4 expression was noted in the primary PAAD tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative to the normal controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eTumor infiltrating immune cells have been recognized as an important component of the tumor microenvironment and play a crucial role in tumor initiation, promotion, progression, and metastasis (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Moreover, cancer-associated fibroblasts have also been implicated in the modulation of various tumor infiltrating immune cells (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Thus, KLF4 expression and immune cell infiltration levels were examined across various TCGA tumor types using TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms.\u003c/p\u003e \u003cp\u003eThe various analyses indicated that KLF4 expression is positively correlated with CD8\u0026thinsp;+\u0026thinsp;T cell infiltration in HNSC-HPV- and UVM, while negatively correlated in LUSC and THYM, based on most of the algorithms (Figure S7). Additionally, KLF4 expression and cancer-associated fibroblast infiltration was found to be positively correlated in BRCA, BRCA-LumA, LIHC, and PCPG, while negatively correlated with HNSC, HNSC-HPV-, STAD and TGCT, based on all or most algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These findings were then confirmed for a subset of the tumors using scatter plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u0026ndash;D). Moreover, KLF4 clinical significance was demonstrated in BLCA, BRCA, CESC, LAML, PAAD, PCPG, SKCM, THCA, UCEC, and UCS via ROC analysis (Figure S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmune microenvironment\u003c/h2\u003e \u003cp\u003eTo evaluate the overall presence of immune cells, the ESTIMATE algorithm was employed. Our analysis revealed a notable decrease in ESTIMATE scores in metastatic tumors when compared to primary tumors (Figure S9), thus indicating a diminished immune cell infiltration in metastatic lesions. Furthermore, KLF4 expression was significantly correlated with immunoinfiltration in 32 cancer species, with 6 being significantly negatively correlated: TCGA-CESC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;291, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.6e-3), TCGA-ESCA (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;181, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.0e-3), TCGA-STES (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;569, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.3e-6), TCGA-HNSC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;517, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.5e-6), TCGA-LUSC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;491, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.6e-3), TCGA-TGCT (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;132, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.3e-5).\u003c/p\u003e \u003cp\u003eRNA modifications can directly affect the chemical properties of the molecule, including its charge, base pairing, secondary structure, and protein-RNA interactions. These modifications can in turn regulate gene expression by controlling RNA processing, localization, translation, and eventual decay (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Herein, m6A (N6-methyladenosine), m1A (N1-methyladenosine), and m5C (5-methylcytosine) were the most common RNA methylation modifications. Pearson\u0026rsquo;s correlation analysis showed that m6A methylation plays a biological role mainly through RNA-binding proteins (Figure S10). Furthermore, when examining correlations between tumor KLF4 expression and immune checkpoint genes as a means to predict immunotherapy applications, a negative correlation between KLF4 and a wide range of immunomodulators was noted (Figure S11). Overall, the top four checkpoint genes associated with KLF4 and correlated with a tumor inhibitory effect in several tumors were C10orf54, CD274, IL10, and TGFB1. While the top three KLF4-assocaited genes correlated with a tumor stimulatory effect for several tumors were CX3CL1, TNFRSF4, and IL1A. Meanwhile, the top three genes which are least correlated with KLF4 and associated with a tumor inhibitory effect in several tumors were BTLA, IDO1, and LAG3. The KLF4-assocaited genes least correlated with a tumor stimulatory effect for several tumors including TNFRSF18.\u003c/p\u003e \u003cp\u003eCells that possess stem-cell like characteristics, stemness, have a higher tendency toward tumorigenic behavior, to include DNA hypermethylation of specific promoters to suppress gene expression (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Thus, to discern potential correlations between KLF4 expression and DNAss, Pearson\u0026rsquo;s correlations were performed based on immune checkpoint genes and KLF4 expression using Sangerbox (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results identified 14 significant associations, with MESO and PCPG being the most negatively correlated and THYM and TGCT being the most positively correlated (Figure S12). The correlation between KLF4 expression and cancer stemness scores (DNAss) based on the Pearson\u0026rsquo;s correlation analysis is shown in Figure S12, with only a few associations achieving statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment of KLF4-related partners\u003c/h2\u003e \u003cp\u003eTo further explore the molecular mechanism of KLF4 in tumorigenesis, KLF4 binding and potential correlated expression was examined using several pathway enrichment analyses. The STRING tool identified a total of 50 experimentally supported binding proteins and an interaction network was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To further expand the list, GEPIA2 was employed and the top 100 genes associated with KLF4 expression were determined using all TCGA tumor expression data (data not shown).\u003c/p\u003e \u003cp\u003eThese STRING and GEPIA2 datasets were then combined and further enrichment analysis was performed using KEGG and GO enrichment analyses. The KEGG analysis identified \u0026ldquo;Cell cycle\u0026rdquo; pathways as associated with KLF4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), while GO analysis identified biological processes such as \"epidermis-development\", \"skin-development\", and \"epidermal-cell-differentiation\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eFurthermore, an intersection analysis of the STING and GEPIA2 datasets identified one common member (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Notably, the KEGG pathways encompassed \"cell cycle,\" while the GO analysis identified \"epidermis development,\" \"skin development,\" and \"epidermal cell differentiation\" in association with the biological processes category\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have demonstrated that KLF4 exhibits a cell type-dependent role in either tumor suppression or promotion (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, KLF4 has been identified as a prognostic predictor for urothelial carcinoma of the bladder, where it regulates TWIST1-mediated epithelial-mesenchymal transition (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, the precise role of the multifunctional KLF4 in the molecular pathogenesis of various tumors remains unclear. Therefore, this study employed a pan-caner approach to identify alterations in KLF4 DNA sequence, gene and protein expression, and DNA methylation in over 30 tumors. Notably, a significant downregulation of KLF4 expression was observed in BLCA, BRCA, LUAD, OV, SKCM, UCEC, and UCS. To our knowledge, this is the first pan-cancer study to characterize KLF4 genetic and epigenetic variations in various human malignant tumors.\u003c/p\u003e \u003cp\u003eTo identify differential KLF4 total protein expression between various primary and tumor samples the CPTAC database was employed (Figure S2). KLF4 was upregulated in PAAD relative to the normal control, while it was significantly downregulated in HNSC and LUAD. Furthermore, DSS analysis revealed that low KLF4 expression is associated with a poor prognosis for KIRP and KIRC. However, in LUAD and UVM, high levels of KLF4 expression were associated with an unfavorable prognosis.\u003c/p\u003e \u003cp\u003eOverall, this comprehensive investigation of KLF4 across multiple cancer types reveals its substantial clinical significance in terms of prognosis, protein expression, and immune cell infiltration in some cancers. Moreover, KLF4 appears to exert a crucial role in both cancer cell stemness and tumor development. Since KLF4 has been implicated as a potential immune-related molecule, it might serve as a potential prognostic marker. These findings indicate that further examination into the role of KLF4 in tumorigenesis is warranted to gain a deeper understanding into its role in the tumor microenvironment.\u003c/p\u003e \u003cp\u003eIn a previous study examining colorectal cancer, downregulated KLF4 expression was associated with a poor prognosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Herein, a similar association was observed in COAD and READ, but the association did not achieve statistical significance following OS/DSS analysis when combining the cohorts (Figure S12). Furthermore, while immune checkpoint upregulation is commonly linked to a poor prognosis, these tumors are more likely to demonstrate a favorable response to immune checkpoint inhibitors. The findings indicated that KLF4 should be considered a target of interest. Thus further characterizing the role of KLF4 will enhance our comprehension of the regulatory pathways governing immune checkpoint modulation and potentially enhance the success rate and effectiveness of immune checkpoint therapy (ICT). Furthermore, this study reinforces the results of previous meta-analyses (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), which found that KLF4 expression is not directly associated with the prognosis of all types of tumors. In other words, KLF4 operates through different mechanisms in different tumor types.\u003c/p\u003e \u003cp\u003eTo further examine the mechanisms of KLF4, immune checkpoint genes were examined in association with KLF4 expression. Herein, several KLF4-assocaited genes, C10orf54 and CD274 were correlated with tumor suppression; while others, CX3CL1, TNFRSF4, and IL1A, were correlated with tumor stimulation. In previous studies, BTLA, LAG3, and IDO1 have been identified as immune checkpoint inhibitors (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). KLF4 plays an important role in tumorigenesis via immune regulation and promotes the cytolytic effector function in a subset of CD8 T cells (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). KLF4 has also been shown to function in the re-activation of CD8 T cells in the immune response against cancer (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Collectively, the results suggest the higher expression of KLF4 indicates the positive correlation with cellular markers of immune activation. These results highlight the potential value of KLF4 as a clinical biomarker and a specific therapeutic target.\u003c/p\u003e \u003cp\u003eThe results of the current study show that KLF4 is associated with cancer prognosis and immune infiltration across several different types of cancers, which is consistent with a recent study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Moreover, KLF4 expression is also strongly associated with immune-related gene expression in various cancers and may play a crucial role as a prognostic biomarker for different cancers and possible therapeutic target across several different cancer types.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eAuthors declare no conflict of interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShoukai Yu: Conceptualization, Methodology, Software Lingmei Qian and Liling Xu: Data curation, Writing- Original draft preparation. Jun Ma: Visualization, Investigation, Supervision. Shoukai Yu: Software, Validation.: All authors: Writing- Reviewing and Editing.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eAll of data in this study are publicly available as described.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShi M, Cui J, Du J, Wei D, Jia Z, Zhang J, et al. A novel KLF4/LDHA signaling pathway regulates aerobic glycolysis in and progression of pancreatic cancer. Clin cancer research: official J Am Association Cancer Res. 2014;20(16):4370\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon BS, Bai J, Cai M, Liu C, Shi J, Lu W. Kruppel-like factor 4-dependent Staufen1-mediated mRNA decay regulates cortical neurogenesis. Nat Commun. 2018;9(1):401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu T, Chen X, Zhang W, Liu J, Avdiushko R, Napier DL, et al. KLF4 regulates adult lung tumor-initiating cells and represses K-Ras-mediated lung cancer. Cell Death Differ. 2016;23(2):207\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie VK, Li Z, Yan Y, Jia Z, Zuo X, Ju Z, et al. DNA-Methyltransferase 1 Induces Dedifferentiation of Pancreatic Cancer Cells through Silencing of Kr\u0026uuml;ppel-Like Factor 4 Expression. Clin cancer research: official J Am Association Cancer Res. 2017;23(18):5585\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinawi T, Hill VK, Krex D, Schackert G, Gentle D, Morris MR, et al. DNA methylation profiles of long- and short-term glioblastoma survivors. Epigenetics. 2013;8(2):149\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara K, Shahmoradgoli M, Mart\u0026iacute;nez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteven A, Seliger B. The Role of Immune Escape and Immune Cell Infiltration in Breast Cancer. Breast care (Basel. Switzerland). 2018;13(1):16\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaccara S, Ries RJ, Jaffrey SR. Reading, writing and erasing mRNA methylation. Nat Rev Mol Cell Biol. 2019;20(10):608\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Li K, Liu C, Yi C. Regulation and functions of non-m(6)A mRNA modifications. Nat Rev Mol Cell Biol. 2023;24(10):714\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhm JE, McGarvey KM, Yu X, Cheng L, Schuebel KE, Cope L, et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet. 2007;39(2):237\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTseng WC, Chuang CW, Yang MH, Pan CC, Tarng DC. Kr\u0026uuml;ppel-like factor 4 is a novel prognostic predictor for urothelial carcinoma of bladder and it regulates TWIST1-mediated epithelial-mesenchymal transition. Urol Oncol. 2016;34(11):485. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e15-.e24\u003c/span\u003e\u003cspan address=\"http://.e15-.e24\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSajadi M, Fazilti M, Nazem H, Mahdevar M, Ghaedi K. 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KLF4 transcription factor in tumorigenesis. Cell death discovery. 2023;9(1):118.\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4506656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4506656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGene expression profiling plays a crucial role in understanding the role of Kr\u0026uuml;ppel-like factor 4 (KLF4) in prognosis and the tumor immune microenvironment in various malignancies. The transcription factor KLF4 plays a crucial role in various cellular processes, including cell differentiation, proliferation, and apoptosis. Genetic alterations and aberrant KLF4 expression have been observed in many malignancies, thus suggesting a potential role as a prognostic marker and therapeutic target.\u003c/p\u003e \u003cp\u003eHerein, a systematic analysis of KLF4 genetic alterations revealed the mutation, amplification, and deletion frequencies across different cancer types. The genetic alteration patterns varied across malignancies, thus highlighting the diverse roles of KLF4 in different tumor contexts. Secondly, the prognostic significance of KLF4 expression was assessed in multiple cancers. High expression levels of KLF4 were associated with better clinical outcomes in kidney renal clear cell carcinoma, while low KLF4 expression correlated with a favorable prognosis in certain malignancies.\u003c/p\u003e \u003cp\u003eIn conclusion, the genetic alterations, dysregulated RNA expression, and prognostic implications of KLF4 in malignancies underscore its significance in cancer biology. The findings present will aid in understanding the role of KLF4 in tumor biology and its association with immune responses. Further investigations should focus on further elucidating the functional roles and regulatory mechanisms of KLF4 to further assess its potential as a therapeutic target and predictive biomarker in cancer management.\u003c/p\u003e","manuscriptTitle":"Gene expression profiling, prognosis, and immune microenvironment of KLF4 in malignancies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 13:46:17","doi":"10.21203/rs.3.rs-4506656/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":"ce155776-7b7b-4125-bdfa-e901c94aa5e2","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T16:24:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-18 13:46:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4506656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4506656","identity":"rs-4506656","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}