Expression Characteristics and Biological Functional Role of FLG in Gastric Cancer

Expression Characteristics and Biological Functional Role of FLG in Gastric Cancer | 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 Expression Characteristics and Biological Functional Role of FLG in Gastric Cancer Nan Xia, Hao Li, Linlin Gao, Yuan Yuan, Hong Shang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3889637/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Filaggrin gene (FLG) plays a fundamental role and is associated with tumor malignant progression and maybe used as a new diagnostic biomarker for many cancers. Nevertheless, the characteristics and biological function in Gastric Cancer (GC) have not yet been elucidated. Thus, we focus on FLG expression, the association with immune infiltration and biological functions in GC. Methods The TCGA and GTEx databases were used to identify the mRNA expression of FLG in GC. We used the HPA database to identify the protein expression of FLG in GC. The Cox regression, Kaplan-Meier and nomogram prediction model were used to analysis the relationship between FLG and survival. We also used the logistic regression to analyze the relationship between FLG expressions and pathological features. FLG genetic modification information was derived from the cBioPortal and the GSCALite database. The relationship between FLG expression and microsatellite instability (MSI), DNA methyltransferases, immune-related genes, tumor mutational burden (TMB) were analyzed. The ESTIMATE and other two methods were evaluated the association between FLG expression and the immune infiltrating cells. The protein-protein interactions between Interacting Genes/ proteins (STRING) were established using the Search Tool. The FLG pathways were analyzed using GO and KEGG enrichment analyses. The ceRNA networks were identified in TCGA database. We performed differential expression of FLG and explored the biological role in tumor malignant progression of GC cells. Results We demonstrated that FLG is up-regulated in GC cells and significantly related with worse prognosis. Genetic alterations may lead to abnormal expression of FLG. Meanwhile, the expression of FLG was strongly correlated with immune characteristics. Moreover, FLG has many molecular functions and participates in many signaling pathways. In the cytology experiments, we found that silencing FLG expression largely inhibits GC cell metastasis via epithelial-mesenchymal transition (EMT) signaling pathway. Conclusion FLG is a novel and useful biomarker for prognosis, immune infiltration and malignant progression of GC. FLG prognosis malignant progression epithelial-mesenchymal transition gastric cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Cancer is one of the important causes of high mortality in China and developed countries [ 1 – 2 ] . Although breakthrough advances have been made in cancer treatment over the past few decades with applications including targeted therapies and immunotherapies, most therapies are beneficial for patients, and the efficacy remains limited in most cancers [ 3 – 7 ] . In addition, clinical trials related to biomarkers remain challenging in most cancers. Therefore, it is urgent to develop new biomarkers for exploring tumor development, disease prognosis, diagnosis and treatment. More importantly, in order to discover new therapies for malignant tumors, new targets of genetic alterations must be identified. GC is one of the most common malignancies. Although targeted therapies and immunotherapies have prolonged the GC patients’ survival in recent years, the incidence and mortality rates of GC patients remain high [ 2 ] . Gastric adenocarcinoma (STAD) accounts for 95% of GC and the five year survival rate is less than 30% [ 9 ] . The occurrence and progression of GC is known to be a multi-gene process. Therefore, the development of specific and reliable biomarkers is very vital for GC. Filaggrin (FLG) is a gene closely related to cell differentiation. Filaggrin gene is translated and expressed as Keratin microfilament precursor (Profilagrin), which generates Keratin microfilament egg (Filaggrin) through Kallikrein5 (KLK5) [ 10 ] . The FLG gene is prone to mutation, and its mutation or deletion leads to a decrease in the production of Filaggrin, which is the fundamental cause of some diseases [ 11 ] . Mutations in FLG are associated with skin diseases and various cancers [ 11 – 13 ] . Therefore, FLG mutations are considered to be one of the risk factors for various cancers. However, the FLG expression, prognostic and its biological functions remain to be clarified. In our article, we analyzed the expression of FLG in GC cell lines conjunction with TCGA, GTEx and HPA database comprehensively. The prognostic and pathological parameters of FLG was analyzed in GC based on the UCSC. Furthermore, we investigated the genetic alteration of FLG in GC. Besides, we identified the FLG immune infiltration level in GC. Furthermore, we also constructed FLG ceRNA regulatory networks in GC. Last but not the least, we determined the expression of FLG through a series of experiments, and investigated its biological function in tumor malignant progression in GC cells. Materials and Methods Data acquisition and processing We downloaded the gene expression profiles and paired clinical information from TCGA database. FLG expression analysis We investigated FLG expression by using TIMER2, HPA, UALCAN and CPTAC dataset. PrognoScan Analysis We used the PrognoScan database to analysis the survival. Genetic alteration analysis The cBioPortal and GSCALite were analyzed FLG genetic alterations. Mutation for FLG from TCGA were analyzed. Immune infiltration analysis We obtained the tumor dataset from the UCSC database. TIMER2 was used to investigated the correlation between FLG expression and infiltration. Construction of ceRNA networks TargetScan, miRDB, miraid databases were used to predict the ceRNA network of FLG online. FLG-related gene enrichment and protein network GeneMANIA was used to find functionally similar genes to FLG. FLG co-expression protein network was obtained by STRING. Cell culture The human GC cell lines BGC-823, NCI-N87, AGS, MKN45 and HGC27 were maintained in our laboratory, and GES-1 was generated at Beijing Cancer Hospital. Plasmids construction and cell transfection All siRNAs plasmids were purchased from JiKai and transfected into AGS and MKN45 cells by using Lipofectamine 3000. RNA extraction and qRT-PCR We extracted total RNA with Trizol and generated cDNA using a reverse transcription kit. mRNA products and specific primers were performed qRT-PCR. ß-actin was used as an endogenous control. Experiments was repeated three times. Western blotting analysis We conducted WB experiments according to the kit instructions. Experiments was repeated three times. CCK8 detection GC cells transfected with plasmid were seed in 96-well plates for each group. After incubation with time. Flow cytometry assay The samples were stained with Annexin V-FITC and propidium iodide. Flow cytometer was used to display the apoptotic cell population. Transwell assay Cell migration and invasion were proved in BD Bio-coat with or not with matrigel invasion chambers. Wound healing assay Cell migration was also examined using a scratch wound healing assay. Cells were evaluated at different time using an microscope. Statistical analysis We used p -values to analysis the PrognoScan and GEPIA databases according to a log rank test. The correlation strength was categorized according to R values. Data were analyzed using GraphPad Prism and SPSS (version 20.0). Data were reported as means ± SD. p ≤ 0.05 was considered statistically significant. Table 1 Cancer abbreviations and the corresponding full name Abbreviation Cancer Type ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme 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 LGG Brain Lower Grade Glioma 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 STAD Stomach adenocarcinoma SKCM Skin Cutaneous Melanoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma Results 3.1 The Expression of FLG in Gaastric Cancer First of all, we used the TCGA database to detect the expression of FLG in different malignant tumors. FLG levels were down-regulated in STAD and other 10 kinds of tumors. In contrast, FLG was up-regulated levels in CHOL and LUSC ( Fig. 1 A ) . Next, we used TCGA and GTEx to analysis FLG expression in 33 tumors and found that FLG expression was down-regulated in 25 tumors compared with paired normal tissues ( Fig. 1 B ) . In addition, we analyzed FLG expression between various tumors and paired normal specimens. Most tumor tissues have lower FLG expression than paired normal tissues, for example, BRCA, KIRC, LIHC, STAD, THCA and UCEC ( Fig. 1 C ) . In addition, FLG was lowly expressed in STAD with TCGA and GTEx datasets ( Figs. 1 D–F ) . However, the GEO datasets GSE66229 showed that FLG was highly expressed in GC samples ( Figs. 1 G ). Based on the HPA database, FLG protein was less weakly expressed in STAD than stomach normal tissue from the HPA database ( Fig. 1 H ) . However, qRT-PCR analysis of GES1 and GC cells (BGC823, N87, MKN45, HGC27 and AGS) validated high expression of FLG in GC cell lines ( p < 0.001) ( Fig. 1 J ) . Using western blotting the same results were obtained ( Fig. 1 K ) . Finally, the AUC for FLG was 0.730, suggesting that FLG has a remarkable diagnostic value for GC ( Fig. 1 I ) . 3.2 Association of FLG Expression with Clinical Characteristics UALCAN was used to analysis the pathological characteristics, we proved that the subgroup analysis of cancer gender, histologic grade, pathologic T stage, DSS event and PFI event has lower expression of FLG in GC patients than in the normal group ( Figs. 2 A-E ) . Logistic analysis showed that FLG was increased expression in GC and correlated with gender (OR = 0.627, p = 0.036) ( Table 2 ) . Next, we used TCGA database to determine the pathological feature of FLG in GC. Details clinical data are provided in Table 3 . 3.3 Prognostic Value of FLG in GC We used the Kaplan–Meier survival curves to analyzed the connection between FLG expression and prognosis in GC patients. Figures 3 A showed that the expression of FLG was opposite to the prognosis in GC. In addition, subgroup analysis was performed high expression of FLG in stage T4 and N1 GC was associated with poor DSS and PFI ( Figs. 3 B-C ) . The subgroups of male GC with high expression of FLG was associated with poor DSS ( Figs. 3 D ) . However, the subgroups of female GC with high expression of FLG was associated with better DSS and PFI ( Figs. 3 E-F ) . 3.4 Protein–Protein Interactions of FLG FLG PPI analysis were performed to explore the potential mechanisms of FLG. The top 20 correlated genes were obtained using STRING datasets and GeneMANIA: KLK5, SPINK5, KRT10, CDSN, FLG2, KRT10, CASP14, LOR, IVL, SPRR1A, DSG1, TGM1, DSC1, JUP, TCHH, PKP1, SPRR1B, DSP, PKP2 and SPINK9 ( Fig. 4 A ) . PPI results showed that the potential mechanisms of FLG in GC ( Fig. 4 B ) . Figure 4 C shows that the volcano plot of enriched pathways. GO enrichment of the STRING datasets indicated that the FLG correlated genes to the top 3 biological processes (BP) of skin development, epidermal cell differentiation and epidermis development; to the top 3 cellular structures (CC) of the cornified envelope, desmosome and intermediate filament; and to the top 3 molecular functions (MF) of structural constituent of skin epidermis, protein binding involved in heterotypic cell-cell adhesion and cell-cell adhesion mediator activity. KEGG analysis showed that FLG was involved in the regulation of staphylococcus aureus infection and acute myeloid leukemia ( Fig. 4 D ) . Moreover, GSEA was used in the regulation of staphylococcus aureus infection and acute myeloid leukemia ( Fig. 4 D ) . Moreover, GSEA was used to analysis KEGG pathway. The top five potentially relevant pathways with statistical significance were obtained through GSEA: HALLMARK EPITHELIAL MESENCHYMAL TRANSITION, HALLMARK APICAL JUNCTION, HALLMARK KRAS SIGNALING DN, HALLMARK MYOGENESIS, HALLMARK APICAL SURFACE ( Fig. 4 E-F ) . At least, we analyzed that the relationship between the expression level of FLG and the activity of 10 well-known tumor related pathways using pie chart in GSCALite: activated EMT pathway and RTK_A pathway, inhibited DNA damage response pathway ( Fig. 4 G ) . The proportion of tumors with significant impact of FLG on pathway in GSCALite were shown in Fig. 4 H. 3.5 Genetic Variation Analysis and associated with TMB and survival prognosis of FLG in GC In order to study the genetic alteration of FLG in GC, we used the cBioPortal and GSCALite to analyze the gene alteration and mutation. The results showed that the top high gene alteration frequency of FLG occurred in SKCM, LUAD, UCEC, STAD and ESAD ( Fig. 5 A ) . The mutation types and sites of the FLG were shown in Fig. 5 B. SNV Oncoplot, CNV and mRNA RSEM, methylation and gene expression were analyzed on GSCALite. The results showed that the promoter methylation level of FLG was positively associated to FLG expression in 16 tumors including STAD ( Fig. 5 C ) , indicating that high DNA methylation may cause the overexpression of FLG in STAD. Moreover, top 30 frequently mutated genes were defined in GC samples from TCGA and ICGC cohort, and FLG was one of the top frequently mutated genes ( Fig. 5 D ) . FLG and others 20 frequently mutated genes were covered TCGA and ICGC ( Fig. 5 E ) . Patients with mutant genes had significantly higher TMB ( Fig. 5 F ) . We also performed Kaplan-Meier analysis to determine whether TMB was related to the prognosis in GC. FLG mutation ( p = 0.033) was associated with a positive prognosis ( Fig. 5 G ) . Finally, we analyzed the correlation between mutation status and prognosis of GC using the cBioPortal, the results showed that mutant type expression of FLG had favorable STAD ( Fig. 5 H ) . 3.6 Correlation of FLG and Immune Cells TIMER database was used to compare the expression of FLG with various immune cells in GC. Amounting of immune cells, including NK cells, Tcm, Tem, TFH, pDC, B cells, Eosinophils, iDC, macrophages, T cells, Th17 and Th2, were express FLG and linked with disease progression ( Fig. 6 A ) . The infiltration level of most immune cells, including TFH, Tcm, Tem, NK cells, pDC, iDC, DCs, mast cells, macrophages, B cells, Eosinophils and cytotoxic cells were positively correlated with FLG expression ( Fig. 6 B ) . We next use CIBERSORT to evaluate the association of FLG mutations with tumor-infiltrating immune cells in the GC microenvironment. As shown in Fig. 6 C, the results show a significant variation in the composition of the 22 immune cells in each sample. Besides, we found that FLG wild type static state has more activated mast cells ( Fig. 6 D ) . Furthermore, B cell and CD4 T cell were more enriched in FLG wild type GC patients ( Fig. 6 E ) . The six main types of infiltrated immune cells are associated with different CNV types in FLG in GC patients ( Fig. 6 F ) . Kaplan-Meier survival analysis in GC patients between six major types of infiltrating immune cells and FLG expression. Macrophages are positively correlated with low expression of FLG ( Fig. 6 G ) . Finally, the correlation between FLG expression and tumor microenvironment is shown for the Stromal, Immune and ESTIMATE score. FLG expression is positively correlated with Stromal and ESTIMATE score in GC ( Fig. 6 H-I ) . 3.7 Correlation of FLG expression with Lymphocyte, Immunomodulators, Chemokine, Receptor and MHC Based on our results, FLG is associated with lymphocytes, immunomodulators, chemokines, receptors and MHC, which play important roles in tumor immune processes. For example, FLG level was tightly related to Tem CD4, Mast, Mem B, Act B, CD56dim and Monocyte ( Fig. 7 A ) . Meanwhile, FLG expression was also closely associated with Immunomodulators, including ENTPD1, IL6R, CD28, TNFSF13, TNFRSF14, TGFBR1, BTLA, LGALS9 and PVRL2 ( Fig. 7 B-C ) . In addition, chemokine and receptor were closely related to FLG expression, such as CXCL12, CCL14, CCL19, CCL21, CXCL16, CXCL3, CCL28, CCL20, CX3CR1 and CCR4 ( Fig. 7 D-E ) . Finally, we analyzed the expression of FLG and MHC and found that HLA-C, TAP1, HLA-A, HLA-B, HLA-F and TAPBP were closely related to FLG expression ( Fig. 7 F ). 3.8 Construction of regulatory network for FLG-associated ceRNA ​ More and more evidence has been proved for regulatory effects on the ceRNA network in GC. We used volcano plots describe the DElncRNAs, DEmiRNAs and DEmRNAs in STAD with TCGA ( Fig. 8 A-C ) . The Venn diagram was used to the 12 overlapping miRNAs in the Targerscan, mirnaid, and miRDB databases ( Fig. 8 D ) . Four human-derived FLG most related miRNAs (miR-6779-3p, miR-4672-3p, miR-26a-1-3p and miR-1301-3p) and five top-related lncRNA (FLG-AS1, MBNL1-AS1, AC021683.1, MIR1-1HG-AS1 and CARMN) were list in ( Fig. 8 E ) . Ten FLG-related ceRNA regulatory networks in GC were constructed ( Fig. 8 F ) . The miRNAs (miR-6779-3p, miR-26a-1-3p and miR-1301-3p) were verified to negatively correlate with FLG expression ( Fig. 8 G ) . The lncRNAs (FLG-AS1, MBNL1-AS1, AC021683.1, MIR1-1HG-AS1 and CARMN) were verified to positively correlate with FLG expression ( Fig. 8 H ) . The scatter plot were used to display the expression of lncRNAs and miRNAs ( Figs. 8 I-K ) . 3.9 The effects of FLG on proliferation and invasion of GC cells We explore the role of FLG in the GC. First, three shRNA knockout vectors for FLG were constructed and transfected into AGS and MKN45 GC cells. qRT-PCR and western blotting showed variability respectively. According to the results, siFLG-3 had the highest knockout efficiency ( Fig. 9 A-D ) . In addition, we used the CCK8 kit to detect the changes of cell activity, the negative carrier group and the transfected siFLG-3 group at different time points. The results showed that interference with FLG gene expression significantly reduced cell proliferation ( Fig. 9 E ) . Flow cytometry showed that interference with FLG inhibited GC cell apoptosis ( Fig. 9 F ) . Moreover, transwell chamber experiment demonstrated that the invasion ability of GC was obviously weakened after FLG was knocked out ( Figs. 9 G ). Furthermore, the healing ability of GC cells which interfered with FLG expression was significantly weakened by cell scratches experiments ( Figs. 9 H ) . EMT signaling pathway was detected using WB, the FLG promotes E-cadherin gene expression and inhibits N-cadherin, vimentin and ZEB1 expression, suggesting that FLG promotes EMT signaling. ( Figs. 9 I ) . Table 2 Association between FLG expression and clinicopathologic parameters by Logistic regression. Characteristics Total (N) OR (95% CI) P value Pathologic T stage (T3&T4 vs. T1&T2) 367 1.312 (0.826–2.084) 0.249 Pathologic N stage (N1&N2&N3 vs. N0) 357 1.127 (0.719–1.764) 0.603 Pathologic M stage (M1 vs. M0) 355 1.110 (0.492–2.504) 0.802 Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II) 352 0.952 (0.626–1.448) 0.819 Gender (Male vs. Female) 375 1.769 (1.153–2.713) 0.009 Age (> 65 vs. <= 65) 371 0.852 (0.565–1.284) 0.444 Histologic grade (G2&G3 vs. G1) 366 1.011 (0.288–3.554) 0.986 Table 3 Correlation between clinicopathological variables and FLG expression. Characteristics Low expression of FLG High expression of FLG P value n 187 188 Pathologic T stage, n (%) 0.064 T1 15 (4.1%) 4 (1.1%) T2 39 (10.6%) 41 (11.2%) T3 78 (21.3%) 90 (24.5%) T4 50 (13.6%) 50 (13.6%) Pathologic N stage, n (%) 0.949 N0 57 (16%) 54 (15.1%) N1 48 (13.4%) 49 (13.7%) N2 36 (10.1%) 39 (10.9%) N3 35 (9.8%) 39 (10.9%) Pathologic M stage, n (%) 0.802 M0 167 (47%) 163 (45.9%) M1 12 (3.4%) 13 (3.7%) Pathologic stage, n (%) 0.552 Stage I 30 (8.5%) 23 (6.5%) Stage II 50 (14.2%) 61 (17.3%) Stage III 76 (21.6%) 74 (21%) Stage IV 18 (5.1%) 20 (5.7%) Gender, n (%) 0.009 Female 79 (21.1%) 55 (14.7%) Male 108 (28.8%) 133 (35.5%) Age, n (%) 0.444 65 108 (29.1%) 99 (26.7%) Histologic grade, n (%) 0.098 G1 5 (1.4%) 5 (1.4%) G2 78 (21.3%) 59 (16.1%) G3 99 (27%) 120 (32.8%) OS event, n (%) 0.262 Alive 119 (31.7%) 109 (29.1%) Dead 68 (18.1%) 79 (21.1%) DSS event, n (%) 0.029 No 139 (39.3%) 124 (35%) Yes 36 (10.2%) 55 (15.5%) PFI event, n (%) 0.017 No 136 (36.3%) 115 (30.7%) Yes 51 (13.6%) 73 (19.5%) Discussion Gastric cancer (GC) is one of the malignant tumors with high mortality and mortality rates in humans [ 14 ] . So far, despite extensive scientific research, the development of stable and effective markers is still limited and the molecular mechanisms remain unclear. Therefore, it is urgent to development more reliable and effective molecular biomarker. In our study, we firstly found that FLG was highly expressed and promoted GC cells proliferation, migration and invasion through regulating EMT signaling pathway. In addition, it was the first time shown that correlation between FLG expression and prognosis among different genders of GC patients by bioinformatics, and the correlation between FLG expression and immune infiltrating cells, inflammatory factors, and immune microenvironment in GC patients had been analyzed. These results indicated that FLG plays an important role in the malignant progression of GC patients with high immune dependence. Filaggrin (FLG) is an acidic or neutral protein that exists in human epidermal keratinocytes and participates in keratinization. The formation of cross junctions between cells during the final differentiation of keratinocytes can break down and produce a large amount of natural moisturizing factors, which can also initiate apoptosis of KC. As a result, FLG plays a key role in skin barrier function and the apoptosis process of keratinocytes. The FLG is located in the epidermal differentiation complex of chromosome lq21.3 and consists of three exons. Exon 3 (12.7-14.7Kb) is the most important functional domain of FLG, encoding the S100 region, B-binding region, and 10–12 silk protein repeat sequences. There are two special regions at the N-end of FLG: the highly conserved S100 calcium binding region and its downstream more conserved B-region [ 15 ] . Adjacent to it are two defective FLG repeat regions, and the intermediate fragment is a repetitive gene sequence consisting of 10–12 FLGs. Profilagrin, a translated and expressed product of the FLG gene, is a highly phosphorylated and insoluble precursor protein. In the late stage of differentiation, Profilagrin rapidly dephosphorylates from inactive fibrinogen to form soluble Filaggrin [ 16 – 18 ] . FLG functional deletion mutations are the most important genetic risk factor for atopic dermatitis [ 19 ] . ​With the development of gene sequencing and bioinformatics techniques, there is a growing body of research suggesting that FLG plays a role in the occurrence and development of tumors. Mohit K. Midha showed that the main somatic mutation susceptibility genes of early-onset breast cancer include TP53, PIK3CA, etc. Compared with conventional breast cancer, EOBC has a higher frequency of somatic mutations in structural protein coding genes, including FLG. Bypass analysis showed that the above somatic mutations and germline mutations involve the interaction pathway of adhesive spots and ECM receptors [ 20 ] . M Cintorino indicated that FLG was more irregular in HPV16,18 compared to HPV6,11,13 cervical lesions [ 21 ] . The research results of C Lara indicated that the expression disorder of FLG was common in cervical precancerous lesions and cervical cancer with lower differentiation [ 22 ] . Weiting Ge and Sergei I indicated that FLG and other 5 genes mutation have the most predictive value in stage III colon cancer, and there may be a correlation between impaired intestinal barrier function and bacterial translocation [ 23 – 24 ] . Hiraku Suga and Magdalena Trzeciak showed that compared to normal skin, FLG mRNA and protein expression were significantly down-regulated in CTCL lesions at different clinical stages such as plaque, erythroderma, and tumor, which were negatively correlated with the progression of CTCL disease [ 25 – 26 ] . Adam I Riker and Zufeng Sheng showed that FLG gene expression is lower in metastatic SKCM compared to primary SKCM. Enrichment analysis showed that genes including FLG were associated with keratinocyte differentiation and epidermal development. And FLG over-expression was significantly correlated with OS [ 27 – 28 ] . Elise P. Salerno indicated that CD45 + immune cell infiltration was negatively correlated with FLG expression in metastatic SKCM [ 29 ] . In addition, survival analysis showed that patients with FLG over-expression and metastatic SKCM have a shorter survival period, and those with low levels of tumor infiltration have poorer prognosis [ 30 – 31 ] . The results of both in vivo and in vitro experiments by Katie M. Leick suggested that FLG/DST can promote tumor growth in melanoma [ 32 ] . Fu Yicheng indicated that GC patients carrying FLG mutations have significantly higher OS and DFS [ 33 ] . In our study, results from the TCGA database showed slightly lower levels of FLG mRNA expression in GC patients compared to normal adjacent tissues. However, FLG mRNA expression is slightly higher in GC patients in the GEO database. Cellular experiments have previously shown an increase in FLG expression levels in GC cell lines. Therefore, the discrepancy between TCGA and GEO databases may be caused by the limited sample counts. The ROC curve suggests FLG maybe a promising diagnostic biomarker to distinguish GC from normal tissues. In GC patients, there were significant differences in FLG expression with gender, histological grade, pathological T stage, DSS, and PFI. In the prognostic analysis, FLG over-expression resulted in a poor prognosis, and in the subgroup analysis, T4 and N1 GC patients were over-expressed, while DSS and PFI were poor; male GC patients with high expression of FLG had poor DSS, but female GC patients with high expression of FLG had better DSS and PFI. In order to prove the biological roles and regulation mechanism of FLG, we performed GO and KEGG, which showed that biological processes include differentiated skin development. Enrichment analysis of the KEGG pathway indicates that it is primarily involved in neuroactive ligand-receptor interactions, a carcinogenic pathway. The GSEA reveals biological pathways associated with ribosomes, including focal adhesion, regulatory cytoskeleton of actin, and ECM receptor interactions. Then, the PPI networks are constructed. We also found that FLG is highly mutated and has a significant effect on GC prognosis and immune infiltration, including CNV variants and missense mutations. It was partially in concordance with previously published studies [ 33 – 34 ] . In addition, we know that immune cells play a key regulatory role in tumorigenesis and development [ 35 ] . In this study, it was found that FLG is strongly correlated with the NK cell, and the NK enrichment scores of cells with high expression of FLG are significantly different. More and more evidence shows that the improvement of NK infiltration or function in tumors is significantly beneficial to the survival of patients [ 36 – 37 ] . Besides, NK cells also secret large amounts of immunomodulators to enhance anti-tumor activity [ 36 ] . To explore the potential immunomodulatory effects of FLG in GC, we identified lymphocyte, immunomodulators, chemokine, receptor and MHC associated with FLG. These results indicate that the up-regulation of FLG in GC is linked to immune cell infiltration. Therefore, we speculate that over-expression of FLG and immune infiltration cells may promote malignant progression in GC cells, and the specific mechanism of action needs to be further investigated. Evidence for the regulatory role of the lncRNA-miRNA-mRNA of FLG ceRNA network in cancer is growing. Based on the results, we construct a ceRNA regulatory network that predicts FLG may regulate several critical pathways of the GC participation mechanism. We conducted further experiments to validate this network. Finally, in-vitro experiments have demonstrated that FLG promotes malignant processes in GC cells, possibly via EMT process, in agreement with previous PPI and GSEA analyses. ​There are still some limitations in the present study. First, while FLG gene has been shown to be of high prognostic value in GC patients, and its biological function and regulatory signaling pathways have been tentatively validated at the cellular level, the understanding of real case samples and detailed regulatory mechanisms is currently limited. In addition, more detailed in-vitro testing techniques using in-vitro combinations could further elucidate the therapeutic role of the FLG gene in GC patients. We need more clinical data to further validate and elucidate the expression and role of FLG in GC and even pan-cancer. Conclusion In summary, to the best of our knowledge, we demonstrated the first time that comprehensively analyzed the expression, prognosis and immune infiltration of FLG in GC though multiple databases, and the biological role and mechanism of FLG were preliminarily demonstrated by cell experiments in GC. We found that FLG is up-regulated in GC lines and associated with poor survival prognosis and adverse clinical features. The expression of FLG in GC may serve as a promising prognostic marker. Moreover, FLG has been shown to be closely related to immunosuppressive cells, TME, and tumor immunity, which may guide individualized immunotherapy of GC. In addition, knockout of FLG expression suppresses GC cell proliferation, migration, invasion, and promotes apoptosis. The results highlight a strong link between ceRNA network processes and the EMT pathway with GC development. Based on these studies, we provide a preliminary foundation for the development of prognostic and immunological biomarkers in GC. Declarations Funding information This work is supported by grants from the National Nature Science Foundation of China (Ref No. 81902958). DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. ETHICS STATEMENT The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of China Medical University (Shenyang, China). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. CONSENT FOR PUBLICATON Not applicable. COMTETING INTERESTS The authors have declared that no conflict of interest exist AUTHOR CONTRIBUTIONS XN conceived the project, wrote the manuscript and managed data acquisition. LH and G LL participated in the data analysis. YY and SH reviewed the manuscript. All authors contributed to the article and approved the submitted version. FUNDING This research was supported by Natural Science Foundation of China [grant number 81902958] Acknowledgments We thank Professor Youyong Lv from Peking University Cancer Hospital for providing our cell line. References Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever increasing importance of cancer as a leading cause of premature death worldwide [J]. Cancer. 2021;127:3029–30. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin. 2021;71:209–49. 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Ajani JA, Lee J, Sano T, Janjigian YY, Fan D, Song S, et al. Gastric adenocarcinoma. Nat Rev Dis Prim. 2017;3:17036. 10.1038/nrdp.2017.36 . Presland RB, Haydock PV, Fleckman P, et al. Characterization of the human epidermal profilaggrin gene. Genomic organization and identification of an S-100-like calcium binding domain at the amino terminus [J]. J Biol Chem. 1992;267:23772–81. Margolis DJ, Mitra N, Wubbenhorst B, et al. Filaggrin sequencing and bioinformatics tools [J]. Arch Dermatol Res. 2020;312:155–8. Bandier J, Ross-Hansen K, Carlsen BC, et al. Carriers of filaggrin gene (FLG) mutations avoid professional exposure to irritants in adulthood [J]. Contact Dermat. 2013;69:355–62. Skaaby T, Husemoen LL, Thyssen JP, et al. Filaggrin lossof-function mutations and incident cancer: a populationbased study [J]. Br J Dermatol. 2014;171:1407–14. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396:635–48. Smith FJ, Irvine AD. Terron-KwiatkowskiA, et a1. Loss of function mutations in the gene encoding filaggrin cause ichthyosisvulgaris [J]. Nat Genet. 2006;38:337–42. Osawa A, Shimizu H. Filaggrin gene defects and the risk of developing allergic disorders [J]. Allergol Int. 2011;60(1):1–9. Sandilands A, Terron KA, Hull PR. et a1. Comprehensive analysis of the gene encoding filaggrin uncovers prevalent and rare mutations in ichthyosisvulgaris and atopic eczema [J]. Nat Genet. 2007;39:650–4. Sandilands A, Sutherland C, Irvine AD. et a1. Filaggrin in the front line: Role in skin barrier function and disease [J]. J Cell Sei. 2009;122(9):1285–94. Baurecht H, Irvine AD, Novak N, et al. Toward a major risk factor for atopic eczema: meta-analysis of filaggrin polymorphism data [J]. J Allergy Clin Immunol. 2007;120(6):1406–12. Midha MK, Huang Y-F, Yang H-H et al. Comprehensive Cohort Analysis of Mutational Spectrum in Early Onset Breast Cancer Patients[J]. Cancers, 2020, 12(8): E2089. Cintorino M, Syrjänen S, Leoncini P, et al. Altered expression of filaggrin in human papillomavirus (HPV) lesions of the uterine cervix [J]. Arch Gynecol Obstet. 1988;241(4):235–47. Lara C, Serra V, Marzo C, et al. Immunohistochemical localization of filaggrin in benign, premalignant and malignant cervical tissue [J]. Arch Gynecol Obstet. 1994;255(2):73–9. Ge W, Hu H, Cai W, et al. High-risk Stage III colon cancer patients identified by a novel five-gene mutational signature are characterized by upregulation of IL-23A and gut bacterial translocation of the tumor microenvironment [J]. Int J Cancer. 2020;146(7):2027–35. Grivennikov SI, Wang K, Mucida D, et al. Adenoma-linked barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth [J]. Nature. 2012;491(7423):254–8. Suga H, Sugaya M, Miyagaki T, et al. Skin barrier dysfunction and low antimicrobial peptide expression in cutaneous T-cell lymphoma[J]. Clin Cancer Research: Official J Am Association or Cancer Res. 2014;20(16):4339–48. Trzeciak M, Olszewska B, Sakowicz-Burkiewicz M, et al. Expression Profiles of Genes Encoding Cornified Envelope Proteins in Atopic Dermatitis and Cutaneous T-Cell Lymphomas [J]. Nutrients. 2020;12(3):E862. Sheng Z, Han W, Huang B, et al. Screening and identification of potential prognostic biomarkers in metastatic skin cutaneous melanoma by bioinformatics analysis [J]. J Cell Mol Med. 2020;24(19):11613–8. Riker AI, Enkemann SA, Fodstad O, et al. The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis [J]. BMC Med Genom. 2008;1:13. Salerno EP, Bedognetti D, Mauldin IS, et al. Human melanomas and ovarian cancers overexpressing mechanical barrier molecule genes lack immune signatures and have increased patient mortality risk[J]. Oncoimmunology. 2016;5(12):e1240857. Bogunovic D, O’Neill DW, Belitskaya-Levy I, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival [J]. Proc Natl Acad Sci USA. 2009;106(48):20429–34. Erdag G, Schaefer JT, Smolkin ME, et al. Immunotype and immunohistologic characteristics of tumor-infiltrating immune cells are associated with clinical outcome in metastatic melanoma [J]. Cancer Res. 2012;72(5):1070–80. Leick KM, Rodriguez AB, Melssen MM, et al. The Barrier Molecules Junction Plakoglobin, Filaggrin, and Dystonin Play Roles in Melanoma Growth and Angiogenesis [J]. Ann Surg. 2019;270(4):712–22. Yicheng F, Xin L, Tian Y, Huilin L. Association of FLG mutation with tumor mutation load and clinical outcomes in patients with gastric cancer [J]. Front Genet. 2022;13:808542. Wang H, Shen L, Li Y, Lv J. Integrated characterisation of cancer genes identifies key molecular biomarkers in stomach adenocarcinoma. J Clin Pathol. 2020;73:579–86. Garnelo M, Tan A, Her Z, Yeong J, Lim CJ, Chen J, et al. Interaction between tumour-infiltrating b cells and T cells controls the progression of hepatocellular carcinoma. Gut. 2017;66:342–51. Chiossone L, Dumas PY, Vienne M, Vivier E. Natural killer cells and other innate lymphoid cells in cancer. Nat Rev Immunol. 2018;18(11):671–88. Laskowski TJ, Biederstadt A, Rezvani K. Natural killer cells in antitumour adoptive cell immunotherapy. Nat Rev Cancer. 2022;22(10):557–75. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3889637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268707158,"identity":"f7f809c3-8be2-4fe4-a574-8c47c45af50d","order_by":0,"name":"Nan Xia","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Xia","suffix":""},{"id":268707159,"identity":"5c0c8056-7e05-4a14-9b05-0e951e250196","order_by":1,"name":"Hao Li","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":268707160,"identity":"fd532e32-b2c6-4935-a42a-556004f17b9f","order_by":2,"name":"Linlin Gao","email":"","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Gao","suffix":""},{"id":268707161,"identity":"858c2b4a-c617-4ad8-90dd-ce3f4b36a197","order_by":3,"name":"Yuan Yuan","email":"","orcid":"","institution":"The First Hospital of China Medical University, China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yuan","suffix":""},{"id":268707162,"identity":"6d9c2e79-c333-4371-89af-10d1a178a955","order_by":4,"name":"Hong Shang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAxDB2AAkmBkbDnxsAHMbDxCnhZ354MOZDQwSIC6RWvjZko15wVoYGPBqMWfvPfzy5w67PHlnHjNp2x02dbrth4G21NhE49Ji2XMuzZr3THKx4WGgltwzaRJmZxKBWo6l5TbgctiNHDNjxjbmxI3NIC1thyXMDgC1MDYcxqvF8GdbPUSLJUjL+YcEtRg/4G07nDifGeh9RpCWG4RsOXPGjJm37XjiBmZgIPe2pUluuwG0JQGfX473GH/82VadOL//YMOBn202/Gbn0x8++FBjg1MLELCBo8LgALJYAm7lIMD8AUTK4zF0FIyCUTAKRjgAADIBZuBMbd3TAAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Shang","suffix":""}],"badges":[],"createdAt":"2024-01-23 02:45:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3889637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3889637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50177845,"identity":"07112158-cf26-4f7d-b5fd-b619aab328dc","added_by":"auto","created_at":"2024-01-25 17:10:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":376392,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of FLG in STAD. (A-B) FLG expression between different tumor and normal tissues in TCGA and GTEx database. (C) FLG expression between tumor and paired normal specimens. (D-F) TCGA database analysis of FLG expression in STAD. (G) FLG expression in STAD with GEO database. (H) FLG protein in stomach normal and GC were obtained from the HPA database. (I) ROC analysis of FLG genes in TCGA_GTEx database (AUC=1, is a perfect classifier. AUC=[0.85, 0.95], very effective. AUC=[0.7, 0.85], average effect). (J) RT-qPCR analysis of FLG mRNA expression in GES-1, and five GC cell lines (BGC823, N87, MKN45, HGC27 and AGS). (K) FLG protein expression in five GC cell lines and GES-1 by WB (*, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/f9b82761a268f9461686040a.png"},{"id":50177844,"identity":"cfdb0264-00c0-4461-a5b8-009d9ae6636e","added_by":"auto","created_at":"2024-01-25 17:10:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91701,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship of FLG expression in STAD with clinical features. The relationship of FLG expression to gender (A), histological grade (B), pathologic T Stage (C), DSS (D) and PFI (E) event in STAD. (*, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/82040ee955a5ac2b47bb4089.png"},{"id":50178229,"identity":"f1f7ebe9-f95e-4d11-bdc2-d5c9ea76d790","added_by":"auto","created_at":"2024-01-25 17:18:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144872,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival plots between FLG expression and prognosis in STAD. (A) Survival curves between FLG different expression GC patients in TCGA. (B) Survival curves DSS in GC patients at T4 stage. (C) Survival curves comparing PFI in patients. (D) Survival curves DSS in GC patients at Male. (E-F) Survival curves DSS and PFI in GC patients at Female.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/b058ef7b2c7b1c3c84c3e12f.png"},{"id":50177851,"identity":"600d0eb2-22a8-420d-b29e-bc0f0f2d26f2","added_by":"auto","created_at":"2024-01-25 17:10:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":751682,"visible":true,"origin":"","legend":"\u003cp\u003eFLG related gene network, GO enrichment, KEGG pathway and protein–protein interactions in STAD from the LinkedOmics database. (A) PPI network of 20 experimentally verified FLG-interacted proteins using the STRING and GeneMANIA. (B) PPI network of FLG-interacted proteins in STAD cohort. (C) Volcano plot of enriched pathways. (D) The correlations between FLG and BP, CC, and MF according to GO enrichment. KEGG analysis was performed based on the FLG-interacted and correlated genes. (E) The top five KEGG pathway of FLG in STAD. (F) The GSEA of the top five KEGG pathway of FLG in STAD. (G) The relationship between the expression level of FLG gene and the activity of 10 well-known tumor related pathways in GSCALite. (H) The proportion of tumors with significant impact of FLG gene on pathway activity in GSCALite.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/4b7ee7061c8ae91b9c266e43.png"},{"id":50177847,"identity":"f663f648-4d5c-4b58-bfce-9268f18038c6","added_by":"auto","created_at":"2024-01-25 17:10:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":982216,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alterations of FLG associated with TMB and clinical prognosis in STAD. (A) FLG alteration frequency in TCGA. (B) The mutation types and sites of the FLG. (C) SNV oncoplot, CNV and DNA methylation in pan-cancer (D) The frequently mutated genes were displayed by waterfall plot in GC from TCGA and ICGC cohort. (E) The frequently mutated genes from TCGA and ICGC cohorts using venn diagram. (F) Correlation between FLG mutation and TMB. (G) Kaplan-Meier survival of GC with FLG mutations. (H) Correlation between mutation status and DSS and PFS using the cBioPortal tool.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/64c4ca9e34107aacc40ecf4a.png"},{"id":50178233,"identity":"a8084f06-525e-486d-a1a6-27312752fb0a","added_by":"auto","created_at":"2024-01-25 17:18:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1066316,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between FLG gene and immune cells in STAD. (A) FLG expression significantly correlated with immune infiltration in GC. (B) FLG expression correlated with infiltration in the TIMER database. (C) FLG mutation is associated with immunoinfiltrating cells. (D) FLG-mutant and wild group differentially infiltrated immune cells were displayed by violin plot. (E) Bar chart displays the six main types of infiltrated immune cells between FLG-mutant and wild group in STAD. (F) Six main types of infiltrated immune cells between CNV type in STAD. (G) Kaplan-Meier survival of GC between the six main types of infiltrated immune cells and FLG expression. (H) Heatmap displayed that the correlation between FLG expression and the Stromal, Immune and ESTIMATE score of the tumor microenvironment in pan-cancer. (I) Correlation between FLG expression and Stromal Score in STAD.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/8abd294013faaa4ed77a5d69.png"},{"id":50178230,"identity":"5a23325e-dd71-48a2-a3c6-7de3c5e06328","added_by":"auto","created_at":"2024-01-25 17:18:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":940175,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation of FLG expression with lymphocyte, immunomodulators, chemokine, receptor and MHC in STAD at TISIDB database. (A) Correlation of FLG expression with Lymphocyte in STAD. (B) Correlation of FLG expression with immune stimulators in STAD. (C) Correlation between the expression of FLG and immune inhibitors. (D) Correlation between the expression of FLG and chemokine. (E) Correlation of FLG expression with receptor in STAD. (F) Correlation of FLG expression with MHC in STAD.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/08a59639dbf37c170f819347.png"},{"id":50178499,"identity":"24d35aa2-732c-429a-a2bc-8887ab5ceca1","added_by":"auto","created_at":"2024-01-25 17:26:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":955134,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction the ceRNA networks of FLG in STAD. (A-C) The volcano plots describe (A) DElncRNAs, (B) DEmiRNAs, and (C) DEmRNAs in STAD with TCGA database. (D) Venn diagram results showed 12 overlapping miRNAs in Targerscan, mirnaid, and miRDB databases. (E) The correlation between five lncRNAs and four miRNAs screened with FLG expression. (F) FLG-related ceRNA regulatory network were shown by Sankey diagram. (G-H) miRNAs or \u0026nbsp;lncRNAs correlated with FLG were shown by Scatter plots. (I-K) LncRNAs correlated with miRNAs were shown by Scatter plots.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/11bf6b4572bc31ca94fd4c6d.png"},{"id":50178231,"identity":"3b5f534f-809e-456a-afbc-15b0120f6643","added_by":"auto","created_at":"2024-01-25 17:18:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1114724,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of FLG in GC cells. (A-D) siRNA–FLG was evaluated in AGS and MKN45 GC cells with qRT-PCR and western blotting. (E) CCK8 assays detected GC cell proliferation. (F) Flow cytometry detected GC cell apoptosis. (G) Transwell assays detected invasion of GC cells. (H) Wound-healing assays detected migration of GC cells. (I) EMT signaling pathway was detected using WB. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/0caa16b7fa4adb062677e846.png"},{"id":50399436,"identity":"132589c3-ee95-4a19-92ad-1f2a3065de63","added_by":"auto","created_at":"2024-01-31 02:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4628682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/ba5d0b1c-5d16-4f07-b9f1-5b5146816001.pdf"},{"id":50177853,"identity":"adaf0b06-37c1-43ab-82a0-87d17ce36b90","added_by":"auto","created_at":"2024-01-25 17:10:43","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":165151,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3889637/v1/b281e4c17e731cd5af391e51.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression Characteristics and Biological Functional Role of FLG in Gastric Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer is one of the important causes of high mortality in China and developed countries \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although breakthrough advances have been made in cancer treatment over the past few decades with applications including targeted therapies and immunotherapies, most therapies are beneficial for patients, and the efficacy remains limited in most cancers \u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In addition, clinical trials related to biomarkers remain challenging in most cancers. Therefore, it is urgent to develop new biomarkers for exploring tumor development, disease prognosis, diagnosis and treatment. More importantly, in order to discover new therapies for malignant tumors, new targets of genetic alterations must be identified.\u003c/p\u003e \u003cp\u003eGC is one of the most common malignancies. Although targeted therapies and immunotherapies have prolonged the GC patients\u0026rsquo; survival in recent years, the incidence and mortality rates of GC patients remain high \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Gastric adenocarcinoma (STAD) accounts for 95% of GC and the five year survival rate is less than 30% \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The occurrence and progression of GC is known to be a multi-gene process. Therefore, the development of specific and reliable biomarkers is very vital for GC.\u003c/p\u003e \u003cp\u003eFilaggrin (FLG) is a gene closely related to cell differentiation. Filaggrin gene is translated and expressed as Keratin microfilament precursor (Profilagrin), which generates Keratin microfilament egg (Filaggrin) through Kallikrein5 (KLK5) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The FLG gene is prone to mutation, and its mutation or deletion leads to a decrease in the production of Filaggrin, which is the fundamental cause of some diseases \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Mutations in FLG are associated with skin diseases and various cancers \u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Therefore, FLG mutations are considered to be one of the risk factors for various cancers. However, the FLG expression, prognostic and its biological functions remain to be clarified.\u003c/p\u003e \u003cp\u003eIn our article, we analyzed the expression of FLG in GC cell lines conjunction with TCGA, GTEx and HPA database comprehensively. The prognostic and pathological parameters of FLG was analyzed in GC based on the UCSC. Furthermore, we investigated the genetic alteration of FLG in GC. Besides, we identified the FLG immune infiltration level in GC. Furthermore, we also constructed FLG ceRNA regulatory networks in GC. Last but not the least, we determined the expression of FLG through a series of experiments, and investigated its biological function in tumor malignant progression in GC cells.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData acquisition and processing\u003c/h2\u003e\n \u003cp\u003eWe downloaded the gene expression profiles and paired clinical information from TCGA database.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eFLG expression analysis\u003c/h2\u003e\n \u003cp\u003eWe investigated FLG expression by using TIMER2, HPA, UALCAN and CPTAC dataset.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003ePrognoScan Analysis\u003c/h2\u003e\n \u003cp\u003eWe used the PrognoScan database to analysis the survival.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic alteration analysis\u003c/h2\u003e\n \u003cp\u003eThe cBioPortal and GSCALite were analyzed FLG genetic alterations. Mutation for FLG from TCGA were analyzed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e\n \u003cp\u003eWe obtained the tumor dataset from the UCSC database. TIMER2 was used to investigated the correlation between FLG expression and infiltration.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of ceRNA networks\u003c/h2\u003e\n \u003cp\u003eTargetScan, miRDB, miraid databases were used to predict the ceRNA network of FLG online.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eFLG-related gene enrichment and protein network\u003c/h2\u003e\n \u003cp\u003eGeneMANIA was used to find functionally similar genes to FLG. FLG co-expression protein network was obtained by STRING.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCell culture\u003c/h2\u003e\n \u003cp\u003eThe human GC cell lines BGC-823, NCI-N87, AGS, MKN45 and HGC27 were maintained in our laboratory, and GES-1 was generated at Beijing Cancer Hospital.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePlasmids construction and cell transfection\u003c/h2\u003e\n \u003cp\u003eAll siRNAs plasmids were purchased from JiKai and transfected into AGS and MKN45 cells by using Lipofectamine 3000.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA extraction and qRT-PCR\u003c/h2\u003e\n \u003cp\u003eWe extracted total RNA with Trizol and generated cDNA using a reverse transcription kit. mRNA products and specific primers were performed qRT-PCR. \u0026szlig;-actin was used as an endogenous control. Experiments was repeated three times.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eWestern blotting analysis\u003c/h2\u003e\n \u003cp\u003eWe conducted WB experiments according to the kit instructions. Experiments was repeated three times.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCCK8 detection\u003c/h2\u003e\n \u003cp\u003eGC cells transfected with plasmid were seed in 96-well plates for each group. After incubation with time.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eFlow cytometry assay\u003c/h2\u003e\n \u003cp\u003eThe samples were stained with Annexin V-FITC and propidium iodide. Flow cytometer was used to display the apoptotic cell population.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eTranswell assay\u003c/h2\u003e\n \u003cp\u003eCell migration and invasion were proved in BD Bio-coat with or not with matrigel invasion chambers.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eWound healing assay\u003c/h2\u003e\n \u003cp\u003eCell migration was also examined using a scratch wound healing assay. Cells were evaluated at different time using an microscope.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe used \u003cem\u003ep\u003c/em\u003e-values to analysis the PrognoScan and GEPIA databases according to a log rank test. The correlation strength was categorized according to R values. Data were analyzed using GraphPad Prism and SPSS (version 20.0). Data were reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCancer abbreviations and the corresponding full name\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCancer Type\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCESC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColon adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEsophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHNSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHead and Neck squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney Chromophobe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKIRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMESO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMesothelioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSKCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTHYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUveal Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n"},{"header":"Results","content":"\u003ch2\u003e3.1 The Expression of FLG in Gaastric Cancer\u003c/h2\u003e\u003cp\u003eFirst of all, we used the TCGA database to detect the expression of FLG in different malignant tumors. FLG levels were down-regulated in STAD and other 10 kinds of tumors. In contrast, FLG was up-regulated levels in CHOL and LUSC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Next, we used TCGA and GTEx to analysis FLG expression in 33 tumors and found that FLG expression was down-regulated in 25 tumors compared with paired normal tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. In addition, we analyzed FLG expression between various tumors and paired normal specimens. Most tumor tissues have lower FLG expression than paired normal tissues, for example, BRCA, KIRC, LIHC, STAD, THCA and UCEC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn addition, FLG was lowly expressed in STAD with TCGA and GTEx datasets \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD–F\u003cb\u003e)\u003c/b\u003e. However, the GEO datasets GSE66229 showed that FLG was highly expressed in GC samples \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e).\u003c/b\u003e Based on the HPA database, FLG protein was less weakly expressed in STAD than stomach normal tissue from the HPA database \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. However, qRT-PCR analysis of GES1 and GC cells (BGC823, N87, MKN45, HGC27 and AGS) validated high expression of FLG in GC cell lines (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ\u003cb\u003e)\u003c/b\u003e. Using western blotting the same results were obtained \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK\u003cb\u003e)\u003c/b\u003e. Finally, the AUC for FLG was 0.730, suggesting that FLG has a remarkable diagnostic value for GC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.2 Association of FLG Expression with Clinical Characteristics\u003c/h2\u003e\u003cp\u003eUALCAN was used to analysis the pathological characteristics, we proved that the subgroup analysis of cancer gender, histologic grade, pathologic T stage, DSS event and PFI event has lower expression of FLG in GC patients than in the normal group \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-E\u003cb\u003e)\u003c/b\u003e. Logistic analysis showed that FLG was increased expression in GC and correlated with gender (OR = 0.627, \u003cem\u003ep\u003c/em\u003e = 0.036) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Next, we used TCGA database to determine the pathological feature of FLG in GC. Details clinical data are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003e3.3 Prognostic Value of FLG in GC\u003c/h2\u003e\u003cp\u003eWe used the Kaplan–Meier survival curves to analyzed the connection between FLG expression and prognosis in GC patients. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA showed that the expression of FLG was opposite to the prognosis in GC. In addition, subgroup analysis was performed high expression of FLG in stage T4 and N1 GC was associated with poor DSS and PFI \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C\u003cb\u003e)\u003c/b\u003e. The subgroups of male GC with high expression of FLG was associated with poor DSS \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. However, the subgroups of female GC with high expression of FLG was associated with better DSS and PFI \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.4 Protein–Protein Interactions of FLG\u003c/h2\u003e\u003cp\u003eFLG PPI analysis were performed to explore the potential mechanisms of FLG. The top 20 correlated genes were obtained using STRING datasets and GeneMANIA: KLK5, SPINK5, KRT10, CDSN, FLG2, KRT10, CASP14, LOR, IVL, SPRR1A, DSG1, TGM1, DSC1, JUP, TCHH, PKP1, SPRR1B, DSP, PKP2 and SPINK9 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. PPI results showed that the potential mechanisms of FLG in GC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC shows that the volcano plot of enriched pathways. GO enrichment of the STRING datasets indicated that the FLG correlated genes to the top 3 biological processes (BP) of skin development, epidermal cell differentiation and epidermis development; to the top 3 cellular structures (CC) of the cornified envelope, desmosome and intermediate filament; and to the top 3 molecular functions (MF) of structural constituent of skin epidermis, protein binding involved in heterotypic cell-cell adhesion and cell-cell adhesion mediator activity. KEGG analysis showed that FLG was involved in the regulation of staphylococcus aureus infection and acute myeloid leukemia \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Moreover, GSEA was used in the regulation of staphylococcus aureus infection and acute myeloid leukemia \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Moreover, GSEA was used to analysis KEGG pathway. The top five potentially relevant pathways with statistical significance were obtained through GSEA: HALLMARK EPITHELIAL MESENCHYMAL TRANSITION, HALLMARK APICAL JUNCTION, HALLMARK KRAS SIGNALING DN, HALLMARK MYOGENESIS,\u003c/p\u003e\u003cp\u003eHALLMARK APICAL SURFACE \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F\u003cb\u003e)\u003c/b\u003e. At least, we analyzed that the relationship between the expression level of FLG and the activity of 10 well-known tumor related pathways using pie chart in GSCALite: activated EMT pathway and RTK_A pathway, inhibited DNA damage response pathway \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. The proportion of tumors with significant impact of FLG on pathway in GSCALite were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH.\u003c/p\u003e\u003ch2\u003e3.5 Genetic Variation Analysis and associated with TMB and survival prognosis of FLG in GC\u003c/h2\u003e\u003cp\u003eIn order to study the genetic alteration of FLG in GC, we used the cBioPortal and GSCALite to analyze the gene alteration and mutation. The results showed that the top high gene alteration frequency of FLG occurred in SKCM, LUAD, UCEC, STAD and ESAD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The mutation types and sites of the FLG were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. SNV Oncoplot, CNV and mRNA RSEM, methylation and gene expression were analyzed on GSCALite. The results showed that the promoter methylation level of FLG was positively associated to FLG expression in 16 tumors including STAD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, indicating that high DNA methylation may cause the overexpression of FLG in STAD.\u003c/p\u003e\u003cp\u003eMoreover, top 30 frequently mutated genes were defined in GC samples from TCGA and ICGC cohort, and FLG was one of the top frequently mutated genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. FLG and others 20 frequently mutated genes were covered TCGA and ICGC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Patients with mutant genes had significantly higher TMB \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. We also performed Kaplan-Meier analysis to determine whether TMB was related to the prognosis in GC. FLG mutation (\u003cem\u003ep\u003c/em\u003e = 0.033) was associated with a positive prognosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. Finally, we analyzed the correlation between mutation status and prognosis of GC using the cBioPortal, the results showed that mutant type expression of FLG had favorable STAD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.6 Correlation of FLG and Immune Cells\u003c/h2\u003e\u003cp\u003eTIMER database was used to compare the expression of FLG with various immune cells in GC. Amounting of immune cells, including NK cells, Tcm, Tem, TFH, pDC, B cells, Eosinophils, iDC, macrophages, T cells, Th17 and Th2, were express FLG\u003c/p\u003e\u003cp\u003eand linked with disease progression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The infiltration level of most immune cells, including TFH, Tcm, Tem, NK cells, pDC, iDC, DCs, mast cells, macrophages, B cells, Eosinophils and cytotoxic cells were positively correlated with FLG expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eWe next use CIBERSORT to evaluate the association of FLG mutations with tumor-infiltrating immune cells in the GC microenvironment. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, the results show a significant variation in the composition of the 22 immune cells in each sample. Besides, we found that FLG wild type static state has more activated mast cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Furthermore, B cell and CD4 T cell were more enriched in FLG wild type GC patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. The six main types of infiltrated immune cells are associated with different CNV types in FLG in GC patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Kaplan-Meier survival analysis in GC patients between six major types of infiltrating immune cells and FLG expression. Macrophages are positively correlated with low expression of FLG \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eFinally, the correlation between FLG expression and tumor microenvironment is shown for the Stromal, Immune and ESTIMATE score. FLG expression is positively correlated with Stromal and ESTIMATE score in GC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-I\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.7 Correlation of FLG expression with Lymphocyte, Immunomodulators, Chemokine, Receptor and MHC\u003c/h2\u003e\u003cp\u003eBased on our results, FLG is associated with lymphocytes, immunomodulators, chemokines, receptors and MHC, which play important roles in tumor immune processes. For example, FLG level was tightly related to Tem CD4, Mast, Mem B, Act B, CD56dim and Monocyte \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Meanwhile, FLG expression was also closely associated with Immunomodulators, including ENTPD1, IL6R, CD28, TNFSF13, TNFRSF14, TGFBR1, BTLA, LGALS9 and PVRL2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C\u003cb\u003e)\u003c/b\u003e. In addition, chemokine and receptor were closely related to FLG expression, such as CXCL12, CCL14, CCL19, CCL21, CXCL16, CXCL3, CCL28, CCL20, CX3CR1 and CCR4 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E\u003cb\u003e)\u003c/b\u003e. Finally, we analyzed the expression of FLG and MHC and found that HLA-C, TAP1, HLA-A, HLA-B, HLA-F and TAPBP were closely related to FLG expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003ch2\u003e3.8 Construction of regulatory network for FLG-associated ceRNA\u003c/h2\u003e\u003cp\u003e​ More and more evidence has been proved for regulatory effects on the ceRNA network in GC. We used volcano plots describe the DElncRNAs, DEmiRNAs and DEmRNAs in STAD with TCGA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C\u003cb\u003e)\u003c/b\u003e. The Venn diagram was used to the 12 overlapping miRNAs in the Targerscan, mirnaid, and miRDB databases \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Four human-derived FLG most related miRNAs (miR-6779-3p, miR-4672-3p, miR-26a-1-3p and miR-1301-3p) and five top-related lncRNA (FLG-AS1, MBNL1-AS1, AC021683.1, MIR1-1HG-AS1 and CARMN) were list in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Ten FLG-related ceRNA regulatory networks in GC were constructed \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. The miRNAs (miR-6779-3p, miR-26a-1-3p and miR-1301-3p) were verified to negatively correlate with FLG expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. The lncRNAs (FLG-AS1, MBNL1-AS1, AC021683.1, MIR1-1HG-AS1 and CARMN) were verified to positively correlate with FLG expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. The scatter plot were used to display the expression of lncRNAs and miRNAs \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI-K\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.9 The effects of FLG on proliferation and invasion of GC cells\u003c/h2\u003e\u003cp\u003eWe explore the role of FLG in the GC. First, three shRNA knockout vectors for FLG were constructed and transfected into AGS and MKN45 GC cells. qRT-PCR and western blotting showed variability respectively. According to the results, siFLG-3 had the highest knockout efficiency \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn addition, we used the CCK8 kit to detect the changes of cell activity, the negative carrier group and the transfected siFLG-3 group at different time points. The results showed that interference with FLG gene expression significantly reduced cell proliferation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Flow cytometry showed that interference with FLG inhibited GC cell apoptosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Moreover, transwell chamber experiment demonstrated that the invasion ability of GC was obviously weakened after FLG was knocked out \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG\u003cb\u003e).\u003c/b\u003e Furthermore, the healing ability of GC cells which interfered with FLG expression was significantly weakened by cell scratches experiments \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. EMT signaling pathway was detected using WB, the FLG promotes E-cadherin gene expression and inhibits N-cadherin, vimentin and ZEB1 expression, suggesting that FLG promotes EMT signaling. \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between FLG expression and clinicopathologic parameters by Logistic regression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage (T3\u0026amp;T4 vs. T1\u0026amp;T2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.312 (0.826–2.084)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage (N1\u0026amp;N2\u0026amp;N3 vs. N0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.127 (0.719–1.764)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage (M1 vs. M0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.110 (0.492–2.504)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage (Stage III\u0026amp;Stage IV vs. Stage I\u0026amp;Stage II)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.952 (0.626–1.448)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (Male vs. Female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.769 (1.153–2.713)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026gt; 65 vs. \u0026lt;= 65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852 (0.565–1.284)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade (G2\u0026amp;G3 vs. G1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.011 (0.288–3.554)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between clinicopathological variables and FLG expression.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of FLG\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of FLG\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (10.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (11.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (21.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (24.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (13.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (13.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (16%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (15.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (13.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (13.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (10.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (10.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (9.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (10.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (47%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (45.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (3.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (8.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (6.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (14.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (17.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (21.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (21%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (5.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (5.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (21.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (14.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (28.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (35.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (21.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (22.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (29.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (26.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (21.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (16.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (27%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (32.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS event, n (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (31.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (29.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (18.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (21.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDSS event, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (39.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (35%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (10.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (15.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePFI event, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (36.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (30.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (13.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (19.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGastric cancer (GC) is one of the malignant tumors with high mortality and mortality rates in humans \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. So far, despite extensive scientific research, the development of stable and effective markers is still limited and the molecular mechanisms remain unclear. Therefore, it is urgent to development more reliable and effective molecular biomarker. In our study, we firstly found that FLG was highly expressed and promoted GC cells proliferation, migration and invasion through regulating EMT signaling pathway. In addition, it was the first time shown that correlation between FLG expression and prognosis among different genders of GC patients by bioinformatics, and the correlation between FLG expression and immune infiltrating cells, inflammatory factors, and immune microenvironment in GC patients had been analyzed. These results indicated that FLG plays an important role in the malignant progression of GC patients with high immune dependence.\u003c/p\u003e \u003cp\u003eFilaggrin (FLG) is an acidic or neutral protein that exists in human epidermal keratinocytes and participates in keratinization. The formation of cross junctions between cells during the final differentiation of keratinocytes can break down and produce a large amount of natural moisturizing factors, which can also initiate apoptosis of KC. As a result, FLG plays a key role in skin barrier function and the apoptosis process of keratinocytes. The FLG is located in the epidermal differentiation complex of chromosome lq21.3 and consists of three exons. Exon 3 (12.7-14.7Kb) is the most important functional domain of FLG, encoding the S100 region, B-binding region, and 10\u0026ndash;12 silk protein repeat sequences. There are two special regions at the N-end of FLG: the highly conserved S100 calcium binding region and its downstream more conserved B-region \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Adjacent to it are two defective FLG repeat regions, and the intermediate fragment is a repetitive gene sequence consisting of 10\u0026ndash;12 FLGs. Profilagrin, a translated and expressed product of the FLG gene, is a highly phosphorylated and insoluble precursor protein. In the late stage of differentiation, Profilagrin rapidly dephosphorylates from inactive fibrinogen to form soluble Filaggrin \u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. FLG functional deletion mutations are the most important genetic risk factor for atopic dermatitis \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e​With the development of gene sequencing and bioinformatics techniques, there is a growing body of research suggesting that FLG plays a role in the occurrence and development of tumors. Mohit K. Midha showed that the main somatic mutation susceptibility genes of early-onset breast cancer include TP53, PIK3CA, etc. Compared with conventional breast cancer, EOBC has a higher frequency of somatic mutations in structural protein coding genes, including FLG. Bypass analysis showed that the above somatic mutations and germline mutations involve the interaction pathway of adhesive spots and ECM receptors \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. M Cintorino indicated that FLG was more irregular in HPV16,18 compared to HPV6,11,13 cervical lesions \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The research results of C Lara indicated that the expression disorder of FLG was common in cervical precancerous lesions and cervical cancer with lower differentiation \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Weiting Ge and Sergei I indicated that FLG and other 5 genes mutation have the most predictive value in stage III colon cancer, and there may be a correlation between impaired intestinal barrier function and bacterial translocation \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Hiraku Suga and Magdalena Trzeciak showed that compared to normal skin, FLG mRNA and protein expression were significantly down-regulated in CTCL lesions at different clinical stages such as plaque, erythroderma, and tumor, which were negatively correlated with the progression of CTCL disease \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Adam I Riker and Zufeng Sheng showed that FLG gene expression is lower in metastatic SKCM compared to primary SKCM. Enrichment analysis showed that genes including FLG were associated with keratinocyte differentiation and epidermal development. And FLG over-expression was significantly correlated with OS \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Elise P. Salerno indicated that CD45\u003csup\u003e+\u003c/sup\u003e immune cell infiltration was negatively correlated with FLG expression in metastatic SKCM \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In addition, survival analysis showed that patients with FLG over-expression and metastatic SKCM have a shorter survival period, and those with low levels of tumor infiltration have poorer prognosis \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The results of both in vivo and in vitro experiments by Katie M. Leick suggested that FLG/DST can promote tumor growth in melanoma \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Fu Yicheng indicated that GC patients carrying FLG mutations have significantly higher OS and DFS \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, results from the TCGA database showed slightly lower levels of FLG mRNA expression in GC patients compared to normal adjacent tissues. However, FLG mRNA expression is slightly higher in GC patients in the GEO database. Cellular experiments have previously shown an increase in FLG expression levels in GC cell lines. Therefore, the discrepancy between TCGA and GEO databases may be caused by the limited sample counts. The ROC curve suggests FLG maybe a promising diagnostic biomarker to distinguish GC from normal tissues. In GC patients, there were significant differences in FLG expression with gender, histological grade, pathological T stage, DSS, and PFI. In the prognostic analysis, FLG over-expression resulted in a poor prognosis, and in the subgroup analysis, T4 and N1 GC patients were over-expressed, while DSS and PFI were poor; male GC patients with high expression of FLG had poor DSS, but female GC patients with high expression of FLG had better DSS and PFI.\u003c/p\u003e \u003cp\u003eIn order to prove the biological roles and regulation mechanism of FLG, we performed GO and KEGG, which showed that biological processes include differentiated skin development. Enrichment analysis of the KEGG pathway indicates that it is primarily involved in neuroactive ligand-receptor interactions, a carcinogenic pathway. The GSEA reveals biological pathways associated with ribosomes, including focal adhesion, regulatory cytoskeleton of actin, and ECM receptor interactions. Then, the PPI networks are constructed.\u003c/p\u003e \u003cp\u003eWe also found that FLG is highly mutated and has a significant effect on GC prognosis and immune infiltration, including CNV variants and missense mutations. It was partially in concordance with previously published studies \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In addition, we know that immune cells play a key regulatory role in tumorigenesis and development \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In this study, it was found that FLG is strongly correlated with the NK cell, and the NK enrichment scores of cells with high expression of FLG are significantly different. More and more evidence shows that the improvement of NK infiltration or function in tumors is significantly beneficial to the survival of patients \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Besides, NK cells also secret large amounts of immunomodulators to enhance anti-tumor activity \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. To explore the potential immunomodulatory effects of FLG in GC, we identified lymphocyte, immunomodulators, chemokine, receptor and MHC associated with FLG. These results indicate that the up-regulation of FLG in GC is linked to immune cell infiltration. Therefore, we speculate that over-expression of FLG and immune infiltration cells may promote malignant progression in GC cells, and the specific mechanism of action needs to be further investigated.\u003c/p\u003e \u003cp\u003eEvidence for the regulatory role of the lncRNA-miRNA-mRNA of FLG ceRNA network in cancer is growing. Based on the results, we construct a ceRNA regulatory network that predicts FLG may regulate several critical pathways of the GC participation mechanism. We conducted further experiments to validate this network. Finally, in-vitro experiments have demonstrated that FLG promotes malignant processes in GC cells, possibly via EMT process, in agreement with previous PPI and GSEA analyses.\u003c/p\u003e \u003cp\u003e​There are still some limitations in the present study. First, while FLG gene has been shown to be of high prognostic value in GC patients, and its biological function and regulatory signaling pathways have been tentatively validated at the cellular level, the understanding of real case samples and detailed regulatory mechanisms is currently limited. In addition, more detailed in-vitro testing techniques using in-vitro combinations could further elucidate the therapeutic role of the FLG gene in GC patients. We need more clinical data to further validate and elucidate the expression and role of FLG in GC and even pan-cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, to the best of our knowledge, we demonstrated the first time that comprehensively analyzed the expression, prognosis and immune infiltration of FLG in GC though multiple databases, and the biological role and mechanism of FLG were preliminarily demonstrated by cell experiments in GC. We found that FLG is up-regulated in GC lines and associated with poor survival prognosis and adverse clinical features. The expression of FLG in GC may serve as a promising prognostic marker. Moreover, FLG has been shown to be closely related to immunosuppressive cells, TME, and tumor immunity, which may guide individualized immunotherapy of GC. In addition, knockout of FLG expression suppresses GC cell proliferation, migration, invasion, and promotes apoptosis. The results highlight a strong link between ceRNA network processes and the EMT pathway with GC development. Based on these studies, we provide a preliminary foundation for the development of prognostic and immunological biomarkers in GC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by grants from the National Nature Science Foundation of China (Ref No. 81902958).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of China Medical University (Shenyang, China). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATON\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMTETING INTERESTS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no conflict of interest exist\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXN conceived the project, wrote the manuscript and managed data acquisition. LH and G LL participated in the data analysis. YY and SH reviewed the manuscript. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Natural Science Foundation of China [grant number\u0026nbsp;81902958]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Youyong Lv from Peking University Cancer Hospital for providing our cell line.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever increasing importance of cancer as a leading cause of premature death worldwide [J]. Cancer. 2021;127:3029\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Nat Rev Cancer. 2022;22(10):557\u0026ndash;75.\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":"FLG, prognosis, malignant progression, epithelial-mesenchymal transition, gastric cancer","lastPublishedDoi":"10.21203/rs.3.rs-3889637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3889637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFilaggrin gene (FLG)\u003c/b\u003e plays a fundamental role and is associated with tumor malignant progression and maybe used as a new diagnostic biomarker for many cancers. Nevertheless, the characteristics and biological function in Gastric Cancer (GC) have not yet been elucidated. Thus, we focus on FLG expression, the association with immune infiltration and biological functions in GC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe TCGA and GTEx databases were used to identify the mRNA expression of FLG in GC. We used the HPA database to identify the protein expression of FLG in GC. The Cox regression, Kaplan-Meier and nomogram prediction model were used to analysis the relationship between FLG and survival. We also used the logistic regression to analyze the relationship between FLG expressions and pathological features. FLG genetic modification information was derived from the cBioPortal and the GSCALite database. The relationship between FLG expression and microsatellite instability (MSI), DNA methyltransferases, immune-related genes, tumor mutational burden (TMB) were analyzed. The ESTIMATE and other two methods were evaluated the association between FLG expression and the immune infiltrating cells. The protein-protein interactions between Interacting Genes/ proteins (STRING) were established using the Search Tool. The FLG pathways were analyzed using GO and KEGG enrichment analyses. The ceRNA networks were identified in TCGA database. We performed differential expression of FLG and explored the biological role in tumor malignant progression of GC cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe demonstrated that FLG is up-regulated in GC cells and significantly related with worse prognosis. Genetic alterations may lead to abnormal expression of FLG. Meanwhile, the expression of FLG was strongly correlated with immune characteristics. Moreover, FLG has many molecular functions and participates in many signaling pathways. In the cytology experiments, we found that silencing FLG expression largely inhibits GC cell metastasis via epithelial-mesenchymal transition (EMT) signaling pathway.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFLG is a novel and useful biomarker for prognosis, immune infiltration and malignant progression of GC.\u003c/p\u003e","manuscriptTitle":"Expression Characteristics and Biological Functional Role of FLG in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 17:10:38","doi":"10.21203/rs.3.rs-3889637/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":"e01de4f0-4e85-4c9f-a45c-7bde4ae2bec3","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-31T01:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 17:10:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3889637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3889637","identity":"rs-3889637","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}