Comprehensive evaluation of FKBP10 expression and its prognostic potential in gastric cancer

FKBP10 Promotes the Muscle Invasion of Bladder Cancer via Lamin A Dysregulation

Bladder cancer (BC) is a prevalent urinary malignancy and muscle-invasive bladder cancer (MIBC) is particularly aggressive and associated with poor prognosis. One of MIBC features is the nuclear atypia. However, the molecular mechanism underlying MIBC remains unclear. Here, we find that FKBP10 is significantly upregulated in MIBC tissues and correlated with metastasis and poor outcomes. FKBP10 promotes tumor cell invasion, migration, and metastasis, but not proliferation. Notably, FKBP10 enhances the nuclear atypia of BC cells. Mechanistically, FKBP10 interacts with prelamin A and hinder the nuclear entry of prelamin A, thereby leading to the decrease in the nuclear lamin A, a key factor involved in nuclear atypia. In human BC tissues, nuclear lamin A is downregulated and negatively correlated with FKBP10 expression. Overall, our findings demonstrate that the FKBP10/prelamin A/lamin A axis contributes to MIBC.

Dynamic landscape of m6A modifications and related post-transcriptional events in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder carcinoma (MIBC) is a serious and more advanced stage of bladder carcinoma. N6-Methyladenosine (m6A) is a dynamic and reversible modifications that primarily affects RNA stability and alternative splicing. The dysregulation of m6A in MIBC can be potential target for clinical interventions, but there have been limited studies on m6A modifications in MIBC and their associations with post-transcriptional regulatory processes.

METHODS

Paired tumor and adjacent-normal tissues were obtained from three patients with MIBC following radical cystectomy. The additional paired tissues for validation were obtained from patients underwent transurethral resection. Utilizing Nanopore direct-RNA sequencing, we characterized the m6A RNA methylation landscape in MIBC, with a focus on identifying post-transcriptional events potentially affected by changes in m6A sites. This included an examination of differential transcript usage, polyadenylation signal sites, and variations in poly(A) tail length, providing insights into the broader impact of m6A alterations on RNA processing in MIBC.

RESULTS

The prognostic-related m6A genes and m6A-risk model constructed by machine learning enables the stratification of high and low-risk patients with precision. A novel m6A modification site in the 3' untranslated region (3'UTR) of IGLL5 gene were identified, characterized by a lower m6A methylation ratio, elongated poly(A) tails, and a notable bias in transcript usage. Furthermore, we discovered two particular transcripts, VWA1-203 and CEBPB-201. VWA1-203 displayed diminished m6A methylation levels, a truncated 3'UTR, and an elongated poly(A) tail, whereas CEBPB-201 showed opposite trends, highlighting the complex interplay between m6A modifications and RNA processing. Source code was provided on GitHub ( https://github.com/lelelililele/Nanopore-m6A-analysis ).

CONCLUSIONS

The state-of-the-art Nanopore direct-RNA sequencing and machine learning techniques enables comprehensive identification of m6A modification and provided insights into the potential post-transcriptional regulation mechanisms on the development and progression in MIBC.

Characterize molecular signatures and establish a prognostic signature of gastric cancer by integrating single-cell RNA sequencing and bulk RNA sequencing

Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

Construction of a prognostic risk model for Stomach adenocarcinoma based on endoplasmic reticulum stress genes

OBJECTIVE

Stomach adenocarcinoma (STAD) is caused by malignant transformation of gastric glandular cells and is characterized by a high incidence rate and a poor prognosis. This study was designed to establish a prognostic risk model for STAD according to endoplasmic reticulum (ER) stress feature genes as cancer cells are susceptible to ER stress.

METHODS

The TCGA-STAD dataset was downloaded to screen differentially expressed genes (DEGs). By intersecting DEGs with ER stress genes retrieved from GeneCards, ER stress-related DEGs in STAD were obtained. Kmeans cluster analysis of STAD subtypes and Single sample gene set enrichment analysis (ssGSEA) analysis of immune infiltration were performed. Cox regression analysis was utilized to construct a risk prognostic model. Samples were split into high-risk and low-risk groups according to the median risk score. Survival analysis and Receiver Operating Characteristic (ROC) curves were conducted to assess the validity of the model. Gene set enrichment analysis (GSEA) was performed to investigate differential pathways in the two risk groups. Cox analysis was performed to verify the independence of the risk model, and a nomogram was generated.

RESULTS

A total of 162 ER stress-related DEGs in STAD were identified by bioinformatics analysis. Kmeans cluster analysis showed that STAD was divided into 3 subgroups. The ssGSEA showed that the levels of immune infiltration in subgroups 2 and 3 were significantly higher than subgroup 1. With 12 prognostic genes (MATN3, ATP2A1, NOX4, AQP11, HP, CAV1, STARD3, FKBP10, EGF, F2, SERPINE1, CNGA3) selected from ER stress-related DEGs using Cox regression analysis, we then constructed a prognostic model. Kaplan-Meier (K‑M) survival curves and ROC curves showed good prediction performance of the model. Significant enrichment of genes in the high-risk group was found in extracellular matrix (ECM) receptor interaction. Cox regression analysis combined with clinical factors showed that the risk model could be used as an independent prognostic factor. The prediction correction curve showed that the good prediction ability of the nomogram.

CONCLUSION

The STAD could be divided into three subgroups, and the 12-gene model constructed by ER stress signatures had a good prognostic performance for STAD patients.

Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma

BACKGROUND

The incidence and mortality of gastric cancer ranks fifth and fourth worldwide among all malignancies, respectively. Accumulating evidences have revealed the close relationship between mitochondrial dysfunction and the initiation and progression of stomach cancer. However, rare prognostic models for mitochondrial-related gene risk have been built up in stomach cancer.

METHODS

In current study, the expression and prognostic value of mitochondrial-related genes in stomach adenocarcinoma (STAD) patients were systematically analyzed to establish a mitochondrial-related risk model based on available TCGA and GEO databases. The tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, and drug sensitivity of gastric adenocarcinoma patients were also investigated using R language, GraphPad Prism 8 and online databases.

RESULTS

We established a mitochondrial-related risk prognostic model including NOX4, ALDH3A2, FKBP10 and MAOA and validated its predictive power. This risk model indicated that the immune cell infiltration in high-risk group was significantly different from that in the low-risk group. Besides, the risk score was closely related to TME signature genes and immune checkpoint molecules, suggesting that the immunosuppressive tumor microenvironment might lead to poor prognosis in high-risk groups. Moreover, TIDE analysis demonstrated that combined analysis of risk score and immune score, or stromal score, or microsatellite status could more effectively predict the benefit of immunotherapy in STAD patients with different stratifications. Finally, rapamycin, PD-0325901 and dasatinib were found to be more effective for patients in the high-risk group, whereas AZD7762, CEP-701 and methotrexate were predicted to be more effective for patients in the low-risk group.

CONCLUSIONS

Our results suggest that the mitochondrial-related risk model could be a reliable prognostic biomarker for personalized treatment of STAD patients.

Construction of cancer- associated fibroblasts related risk signature based on single-cell RNA-seq and bulk RNA-seq data in bladder urothelial carcinoma

Background

The ability of cancer-associated fibroblasts (CAFs) to encourage angiogenesis, tumor cell spread, and increase treatment resistance makes them pro-tumorigenic. We aimed to investigate the CAF signature in Bladder urothelial carcinoma (BLCA) and, for clinical application, to build a CAF-based risk signature to decipher the immune landscape and screen for suitable treatment BLCA samples.

Methods

CAF-related genes were discovered by superimposing CAF marker genes discovered from single-cell RNA-seq (scRNA-seq) data taken from the GEO database with CAF module genes discovered by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data from TCGA. After identifying prognostic genes related with CAF using univariate Cox regression, Lasso regression was used to build a risk signature. With microarray data from the GEO database, prognostic characteristics were externally verified. For high and low CAF-risk categories, immune cells and immunotherapy responses were analyzed. Finally, a nomogram model based on the risk signature and prospective chemotherapeutic drugs were examined.

Results

Combining scRNA-seq and bulk-seq data analysis yielded a total of 124 CAF-related genes. LRP1, ANXA5, SERPINE2, ECM1, RBP1, GJA1, and FKBP10 were the seven BLCA prognostic genes that remained after univariate Cox regression and LASSO regression analyses. Then, based on these genes, prognostic characteristics were created and validated to predict survival in BLCA patients. Additionally, risk signature had a strong correlation with known CAF scores, stromal scores, and certain immune cells. The CAF-risk signature was identified as an independent prognostic factor for BLCA using multifactorial analysis, and its usefulness in predicting immunotherapy response was confirmed. Based on risk classification, we projected six highly sensitive anticancer medicines for the high-risk group.

Conclusion

The prognosis of BLCA may be accurately predicted using CAF-based risk signature. With a thorough understanding of the BLCA CAF-signature, it might be able to explain the BLCA patients' response to immunotherapy and identify a potential target for BLCA treatment.

A Novel Biomarker, FKBP10, for Poor Prognosis Prediction in Patients with Clear Cell Renal Cell Carcinoma

Objective

To screen genes associated with poor prognosis of clear cell renal cell carcinoma (CcRCC) from the public databases HPA (Human Protein Atlas), UALCAN, and GEPIA (Gene Expression Profiling Interactive Analysis) and to investigate the expression of FKBP10 in CcRCC and the effect on prognosis of the patients and the biological behavior of CcRCC cells.

Methods

The tumor tissues and adjacent noncancerous tissues of 42 patients with CcRCC diagnosed and treated in our hospital were collected, and the general information of the patients was recorded. FKBP10 expression in the tissues was determined by qRT-PCR and western blot, and its relationship with general information and prognosis of patients was analyzed. Knockdown or overexpression experiments were carried out with the human proximal tubule epithelial cell line HK-2 and CcRCC cell lines Caki-1, 786-O, ACHN, and A498 to verify the relationship between FKBP10 expression and cell proliferation and adhesion ability using MTT assay and fibronectin adhesion assay, respectively. Western blot was utilized to examine the protein expression level of c-Myc, cyclin D1, and Bcl-2 in the cells.

Results

FKBP10 was highly expressed in CcRCC tissues and cells and was correlated with poor prognosis. In addition, FKBP10 expression was positively correlated with CcRCC tumor size and staging and negatively correlated with tumor differentiation. Moreover, knockdown of FKBP10 significantly inhibited the proliferation of CcRCC cells, notably declined the protein expression of c-Myc, cyclin D1, and Bcl-2, and promoted cell adhesion.

Conclusion

FKBP10 is highly expressed in CcRCC tissues and cells and is associated with poor prognosis in patients. FKBP10 participated in the occurrence and development of CcRCC by promoting cell proliferation and inhibiting apoptosis and adhesion.

Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma

Esophageal cancer is one of the most commonly diagnosed malignant gastrointestinal tumors. The aim of the study was to explore the diagnostic values of anti-POSTN and anti-TIMP1 autoantibodies in esophageal squamous cell carcinoma (ESCC). Differentially expressed genes (DEGs) associated with esophageal cancer were screened out by the LIMMA method in the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Search Tool for the Retrieval of Interacting Genes (STRING) was used to construct the protein-protein interaction (PPI) based on highly DEGs. The candidate hub genes were the intersection genes calculated based on degree and Maximal Clique Centrality (MCC) algorithms via Cytoscape. A total of 370 participants including 185 ESCC patients and 185 matched normal controls were enrolled in enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of autoantibodies corresponding to POSTN and TIMP1 proteins. A total of 375 DEGs with high expression were obtained in esophageal cancer. A total of 20 hub genes were acquired using the cytoHubba plugin by degree and MCC algorithms. The expression levels of anti-POSTN and anti-TIMP1 autoantibodies were higher in the sera of ESCC patients (p < 0.05). Anti-POSTN autoantibody can diagnose ESCC patients with an AUC of 0.638 at the specificity of 90.27% and sensitivity of 27.57%, and anti-TIMP1 autoantibody can diagnose ESCC patients with an AUC of 0.585 at the specificity of 90.27% and sensitivity of 20.54% (p < 0.05). In addition, anti-POSTN and anti-TIMP1 autoantibodies can distinguish ESCC patients from normal controls in most clinical subgroups (p < 0.05). In conclusion, anti-POSTN and anti-TIMP1 autoantibodies may be considered the potential biomarkers in the clinical diagnosis of ESCC.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

A tumour-resident Lgr5+ stem-cell-like pool drives the establishment and progression of advanced gastric cancers

Gastric cancer is among the most prevalent and deadliest of cancers globally. To derive mechanistic insight into the pathways governing this disease, we generated a Claudin18-IRES-CreERT2 allele to selectively drive conditional dysregulation of the Wnt, Receptor Tyrosine Kinase and Trp53 pathways within the gastric epithelium. This resulted in highly reproducible metastatic, chromosomal-instable-type gastric cancer. In parallel, we developed orthotopic cancer organoid transplantation models to evaluate tumour-resident Lgr5⁺ populations as functional cancer stem cells via in vivo ablation. We show that Cldn18 tumours accurately recapitulate advanced human gastric cancer in terms of disease morphology, aberrant gene expression, molecular markers and sites of distant metastases. Importantly, we establish that tumour-resident Lgr5⁺ stem-like cells are critical to the initiation and maintenance of tumour burden and are obligatory for the establishment of metastases. These models will be invaluable for deriving clinically relevant mechanistic insights into cancer progression and as preclinical models for evaluating therapeutic targets.

FK506 binding protein 10: a key actor of collagen crosslinking in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of malignant tumor in the kidney. With numbers of patients whose physical condition or tumor stage not suitable for radical surgery, they only have a narrow choice of using VEGF/mTOR targeted drugs to control their tumors, but ccRCC often shows resistance to these drugs. Therefore, identifying a new therapeutic target is of urgent necessity. In this study, for the first time, we concluded from bioinformatics analyses and in vitro research that FK506 binding protein 10 (FKBP10), together with its molecular partner Lysyl hydroxylase 2 (LH2/PLOD2), participate in the process of type I collagen synthesis in ccRCC via regulating crosslinking of pro-collagen chains. Our findings may provide a potential therapeutic target to treat ccRCC in the future.

High FAM189B Expression and Its Prognostic Value in Patients with Gastric Cancer

The clinical significance of the family with sequence similarity 189 member B (FAM189B) gene remains largely unknown in gastric cancer (GC). A comprehensive investigation combining multiple detection methods was carried out in the current study to unveil the clinical implications and prospective molecular characterization of FAM189B protein and mRNA in GC. The protein level of FAM189B was clearly upregulated in the tumor tissues of GC as compared to noncancerous gastric tissues with 179 GC cases and 147 noncancerous gastric controls assessed by immunohistochemistry. The upregulation of the FAM189B protein was also found in the more deteriorating period of the tumor, as there were increasing trends in the groups of larger tumors, with lymph node metastasis, a further advanced clinical stage, and a higher histological grade. Next, we focused on the mRNA level of FAM189B in GC tissues using various high-throughput data. After the screening of GEO, ArrayExpress, and SRA, we finally achieved 18 datasets, including an RNA sequencing dataset of TCGA. Altogether, 1095 cases of GC tissue samples were collected, with 305 unique examples of noncancerous controls. Concerning the mRNA level of FAM189B in GC, the final standard mean difference (SMD) was 0.46 and the area under the curve (AUC) was 0.79 for the upregulation of FAM189B mRNA, which confirmed that the FAM189B mRNA level was also markedly upregulated in GC tissues and comparable to its protein level. The survival analysis showed that the higher expression of FAM189B was a risk factor for the overall survival, first progression, and postprogression survival of GC. For the Affymetrix ID 1555515_a_at of FAM189B, the higher expression level of FAM189B predicted a lower overall survival, first progression survival, and postprogression survival with the hazard ratio (HR) being 1.56 (1.24, 1.95), 1.69 (1.32, 2.17), and 1.97 (1.5, 2.6), respectively. For the Affymetrix ID 203550_s_at of FAM189B, a similar result could be found with corresponding HR being 1.49 (1.24, 1.8), 1.49 (1.21, 1.83), and 1.66 (1.32, 2.08), respectively. The interaction of MEM, COXPRESdb coexpressed genes, and DEGs of GC finally generated 368 genes, and the pathway of the cell cycle was the top pathway enriched by KEGG. In conclusion, the overexpression of the FAM189B protein and mRNA might enhance the incidence of GC.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization

Gastric Cancer (GC) is a common cancer worldwide with a high morbidity and mortality rate in Asia. Many prognostic signatures from genes and non-coding RNA (ncRNA) levels have been identified by high-throughput expression profiling for GC. To date, there have been no reports on integrated optimization analysis based on the GC global lncRNA-miRNA-mRNA network and the prognostic mechanism has not been studied. In the present work, a Gastric Cancer specific lncRNA-miRNA-mRNA regulatory network (GCsLMM) was constructed based on the ceRNA hypothesis by combining miRNA-target interactions and data on the expression of GC. To mine for novel prognostic signatures associated with GC, we performed topological analysis, a random walk with restart algorithm, in the GCsLMM from three levels, miRNA-, mRNA-, and lncRNA-levels. We further obtained candidate prognostic signatures by calculating the integrated score and analyzed the robustness of these signatures by combination strategy. The biological roles of key candidate signatures were also explored. Finally, we targeted the PHF10 gene and analyzed the expression patterns of PHF10 in independent datasets. The findings of this study will improve our understanding of the competing endogenous RNA (ceRNA) regulatory mechanisms and further facilitate the discovery of novel prognostic biomarkers for GC clinical guidelines.

FKBP10 promotes proliferation of glioma cells via activating AKT-CREB-PCNA axis

Background

Although the availability of therapeutic options including temozolomide, radiotherapy and some target agents following neurosurgery, the prognosis of glioma patients remains poor. Thus, there is an urgent need to explore possible targets for clinical treatment of this disease.

Methods

Tissue microarrays and immunohistochemistry were used to detect FKBP10, Hsp47, p-AKT (Ser473), p-CREB (Ser133) and PCNA expression in glioma tissues and xenografts. CCK-8 tests, colony formation assays and xenograft model were performed to test proliferation ability of FKBP10 in glioma cells in vitro and in vivo. Quantitative reverse transcriptase-PCR, western-blotting, GST-pull down, co-immunoprecipitation and confocal-immunofluorescence staining assay were used to explore the molecular mechanism underlying the functions of overexpressed FKBP10 in glioma cells.

Results

FKBP10 was highly expressed in glioma tissues and its expression was positively correlates with grade, poor prognosis. FKBP10-knockdown suppressed glioma cell proliferation in vitro and subcutaneous/orthotopic xenograft tumor growth in vivo. Silencing of FKBP10 reduced p-AKT (Ser473), p-CREB (Ser133), PCNA mRNA and PCNA protein expression in glioma cells. FKBP10 interacting with Hsp47 enhanced the proliferation ability of glioma cells via AKT-CREB-PCNA cascade. In addition, correlation between these molecules were also found in xenograft tumor and glioma tissues.

Conclusions

We showed for the first time that FKBP10 is overexpressed in glioma and involved in proliferation of glioma cells by interacting with Hsp47 and activating AKT-CREB-PCNA signaling pathways. Our findings suggest that inhibition of FKBP10 related signaling might offer a potential therapeutic option for glioma patients.

FKBP10 Acts as a New Biomarker for Prognosis and Lymph Node Metastasis of Gastric Cancer by Bioinformatics Analysis and in Vitro Experiments

Purpose

To explore the role of FKBP prolyl isomerase 10 (FKBP10) protein in the progression of gastric cancer.

Methods

Four independent gastric cancer databases (GSE27342, GSE29272, GSE54129 and TCGA-STAD) were used to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to identify the abnormally active pathways in patients with gastric cancer. Univariate Cox regression analysis was used to identify genes with stable prognostic value in gastric cancer patients based on three independent gastric cancer databases (GSE15459, GSE62254, TCGA-STAD). Gene set enrichment analysis (GSEA) was used to explore the possible pathways related to FKBP10. The reverse transcription-polymerase chain reaction (RT-PCR) was employed to determine the expression of FKBP10 mRNA in the HGC-27 and MKN-7 cell lines. Adhesion assay was used to detect changes in cell adhesion ability. FKBP10, ITGA1, ITGA2, ITGA5, ITGAV, ITGA6, P-AKT₄₇₃, P-AKT₃₀₈, AKT, and β-actin were evaluated by Western blot (WB).

Results

We first performed differential expression genes (DEGs) screening of four independent GC databases (GSE27342, GSE29272, GSE54129 and TCGA-STAD). Eighty-nine genes showed consistent up-regulation in GC, the results of pathway analysis showed that they were related to “Focal adhesion”. The prognostic value of these 89 genes was tested in three independent GC databases GSE15459, GSE62254 and TCGA-STAD cohort. Finally, 12 genes, in which the expression of FKBP10 was prominently increased in patients with lymph node metastasis (LNM), showed stable prognostic value. The following gene set enrichment analysis (GSEA) also showed that FKBP10 is mainly involved in cell adhesion process, while adhesion experiments confirmed that cell adhesion was down-regulated after silencing FKBP10 in GC cells, and adhesion-related molecules integrin αV and α6 were down-regulated.

Conclusion

FKBP10 may be used as a marker for lymph node metastasis of GC and could be used as a potential target for future treatment of GC.