Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma.

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

Ferroptosis-associated lncRNA prognostic signature predicts prognosis and immune response in clear cell renal cell carcinoma.

Clear cell Renal Cell Carcinoma (ccRCC), the most deadly and life-threatening tumor in the urinary system, has a dismal prognosis and a high risk of metastasizing. Regulation of ferroptosis is a prospective therapeutic target to eradicate malignant cells. Our objective was to seek ferroptosis-associated long non-coding RNAs (FALs) and developed a prediction signature for ccRCC. We extracted transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) databases. Ferroptosis-associated genes (FAGs) were obtained from FerrDb database. A ferroptosis-associated lncRNA prognostic signature (FLPS) of ccRCC was generated utilizing univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, sequentially, based on 8 lncRNAs (LINC00460, AC124854.1, AC084876.1, IGFL2-AS1, LINC00551, AC083967.1, AC073487.1, and LINC02446). The signature's independent predictive value for ccRCC was demonstrated using univariate and multivariate regression analysis (P < 0.05). Subsequently, by combining independent predictive factors, a prognostic nomogram was established. Immunity analysis proclaimed a striking difference in terms of cells, function, checkpoints, and ESTIMATE scores between low- and high-risk groups. Overall, the innovative signature of ferroptosis-associated signatures may have a considerable effect on the immune response and prognosis for ccRCC.

Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning.

OBJECTIVE: To use weighted gene correlation network analysis (WGCNA) and machine learning algorithm to predict classification of early pulmonary nodes with public databases. METHODS: The expression data and clinical data of lung cancer patients were firstly extracted from public database (GTEx and TCGA) to study the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD). The intersection of three R packages (Dseq2, Limma, EdgeR) methods were selected as candidate DEGs for further study. WGCNA was used to obtain relevant modules and key genes of lung cancer classification, GO and KEGG enrichment analysis was performed. The model was built using two machine learning methods, Least Absolute Shrinkage and Selection Operator (LASSO) regression and tumor classification was also predicted with extreme Gradient Boosting (XGBoost) algorithm. RESULTS: DEGs analysis revealed that there were 1306 LUAD genes. WGCNA module analysis showed that a total of 116 genes were significantly related to classification, and module genes were mainly related to 14 KEGG pathways. The machine learning algorithm identified 10 target genes by LASSO regression analysis of differential genes, and 18 genes were identified by XGBoost model. A total of 6 genes were found from the intersection of the above methods as classification signatures of early pulmonary nodules, including "HMGB3" "ARHGAP6" "TCF21" "FCN3" "COL6A6" "GOLM1". CONCLUSION: Using DEGs analysis, WGCNA method and machine learning algorithm, six gene signatures related to early stage of LUAD, which can assist clinicians in disease classification prediction.

The clonal expression genes associated with poor prognosis of liver cancer.

The extensive spatial genomic intratumor heterogeneity (ITH) in liver cancer hindered treatment development and limited biomarker design. Early events that drive tumor malignant transformation in tumor founder cells are clonally present in all tumor cell populations, which provide stable biomarkers for the localization of tumor cells and patients' prognosis. In the present study, we identified the recurrently clonal somatic mutations and copy number alterations (CNAs) (893 clonal somatic mutations and 6,617 clonal CNAs) in 353 liver cancer patients from The Cancer Genome Atlas (TCGA) and evaluated their prognosis potential. We showed that prognosis-related clonal alterations might play essential roles in tumor evolution. We identified 32 prognosis related clonal alterations differentially expressed between paired normal and tumor samples, that their expression was cross-validated by three independent cohorts (50 paired samples in TCGA, 149 paired samples in GSE76297, and 9 paired samples in SUB6779164). These clonal expression alterations were also significantly correlated with clinical phenotypes. Using stepwise regression, we identified five (UCK2, EFNA4, KPAN2, UBE2T, and KIF14) and six (MCM10, UCK2, IQGAP3, EFNA4, UBE2T, and KPNA2) clonal expression alterations for recurrence and survival model construction, respectively. Furthermore, in 10 random repetitions, we showed strong applicability of the multivariate Cox regression models constructed based on the clonal expression genes, which significantly predicted the outcomes of the patients in all the training and validation sets. Taken together, our work may provide a new avenue to overcome spatial ITH and refine biomarker design across cancer types.

Integrated bioinformatics analysis to identify the key gene associated with metastatic clear cell renal cell carcinoma.

Metastasis of clear cell renal cell carcinoma (ccRCC) is a leading cause of death. The purpose of this research was to investigate the key gene in ccRCC tumor metastasis. Three microarray datasets (GSE22541, GSE85258, and GSE105261), which included primary and metastatic ccRCC tissues, were obtained from the Gene Expression Omnibus (GEO) database. Expression profiling and clinical data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) dataset. A total of 20 overlapping differentially expressed genes (DEGs) were identified using the R limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the DEGs were mainly enriched in tumor metastasis-related pathways. Gene expression analysis and survival analysis in the GEPIA2 database further identified the key gene HSD11B2. qRT-PCR result manifested that HSD11B2 level was significantly down-regulated in ccRCC tissues compared with adjacent normal tissues. ROC analysis showed that HSD11B2 exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Univariate and multivariate Cox regression analysis showed that HSD11B2 expression was an independent prognostic factor. To establish a nomogram combining HSD11B2 expression and clinical factors, and a new method for predicting the survival probability of ccRCC patients. Gene Set Enrichment Analysis (GSEA) enrichment results showed that low expression of HSD11B2 was mainly enriched in tumor signaling pathways and immune-related pathways. Immune analysis revealed a significant correlation between HSD11B2 and tumor immune infiltrates in ccRCC. This study suggests that HSD11B2 can serve as a potential biomarker and therapeutic target for ccRCC metastasis.

Pan-Cancer Analysis Reveals the Signature of TMC Family of Genes as a Promising Biomarker for Prognosis and Immunotherapeutic Response.

Transmembrane Channel-like (TMC) genes are critical in the carcinogenesis, proliferation, and cell cycle of human cancers. However, the multi-omics features of TMCs and their role in the prognosis and immunotherapeutic response of human cancer have not been explored. We discovered that TMCs 4-8 were commonly deregulated and correlated with patient survival in a variety of cancers. For example, TMC5 and TMC8 were correlated with the relapse and overall survival rates of breast cancer and skin melanoma, respectively. These results were validated by multiple independent cohorts. TMCs were regulated by DNA methylation and somatic alterations, such as TMC5 amplification in breast cancer (523/1062, 49.2%). Six algorithms concordantly uncovered the critical role of TMCs in the tumor microenvironment, potentially regulating immune cell toxicity and lymphocytes infiltration. Moreover, TMCs 4-8 were correlated with tumor mutation burden and expression of PD-1/PD-L1/CTLA4 in 33 cancers. Thus, we established an immunotherapy response prediction (IRP) score based on the signature of TMCs 4-8. Patients with higher IRP scores showed higher immunotherapeutic responses in five cohorts of skin melanoma (area under curve [AUC] = 0.90 in the training cohort, AUCs range from 0.70 to 0.83 in the validation cohorts). Together, our study highlights the great potential of TMCs as biomarkers for prognosis and immunotherapeutic response, which can pave the way for further investigation of the tumor-infiltrating mechanisms and therapeutic potentials of TMCs in cancer.

Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.

Non-small cell lung cancer (NSCLC) is characterized by relatively rapid response to systemic treatments yet inevitable resistance and predisposed to distant metastasis. We thus aimed at performing sequencing analysis to determine genomic events and underlying mechanisms concerning drug resistance in NSCLC. We performed targeted sequencing of 40 medication-relevant genes on plasma samples from 98 NSCLC patients and analyzed impact of genetic alterations on clinical presentation as well as response to systemic treatments. Profiling of multi-omics data from 1024 NSCLC tissues in public datasets was carried out for comparison and validation of identified molecular events implicated in resistance. A genetic association of CYP2D6 deletion with drug resistance was identified through circulating tumor DNA (ctDNA) profiling and response assessment. FCGR3A amplification was potentially involved in resistance to EGFR inhibitors. We further verified our findings in tissue samples and focused on potential resistance mechanisms, which uncovered that depleted CYP2D6 affected a set of genes involved in EMT, oncogenic signaling as well as inflammatory pathways. Tumor microenvironment analysis revealed that NSCLC with CYP2D6 loss manifested increased levels of immunomodulatory gene expressions, PD-L1 expression, relatively high mutational burden and lymphocyte infiltration. DNA methylation alterations were also found to be correlated with mRNA expressions and copy numbers of CYP2D6. Finally, MEK inhibitors were identified by CMap as the prospective therapeutic drugs for CYP2D6 deletion. These analyses identified novel resistance mechanisms to systemic NSCLC treatments and had significant implications for the development of new treatment strategies.

Identifying CpG methylation signature as a promising biomarker for recurrence and immunotherapy in non-small-cell lung carcinoma.

Epigenetic alterations are crucial to oncogenesis and regulation of gene expression in non-small-cell lung carcinoma (NSCLC). DNA methylation (DNAm) biomarkers may provide molecular-level prediction of relapse risk in cancer. Identification of optimal treatment is warranted for improving clinical management of NSCLC patients. Using machine learning algorithm we identified 4 recurrence predictive CpG methylation markers (cg00253681/ART4, cg00111503/KCNK9, cg02715629/FAM83A, cg03282991/C6orf10) and constructed a risk score model that potently predicted recurrence-free survival and prognosis for patients with NSCLC (P = 0.0002). Integrating genomic, transcriptomic, proteomic and clinical data, the DNAm-based risk score was observed to significantly associate with clinical stage, cell proliferation markers, somatic alterations, tumor mutation burden (TMB) as well as DNA damage response (DDR) genes, and potentially predict the efficacy of immunotherapy. In general, our identified DNAm signature shows a significant correlation to TMB and DDR pathways, and serves as an effective biomarker for predicting NSCLC recurrence and response to immunotherapy. These findings demonstrate the utility of 4-DNAm-marker panel in the prognosis, treatment decision-making and evaluation of therapeutic responses for NSCLC.

ARHGEF38 as a novel biomarker to predict aggressive prostate cancer.

Prostate cancer (PCa) metastasis is considered the leading cause of cancer death in males. Therapeutic strategies and diagnosis for stage-specific PCa have not been well understood. Rho guanine nucleotide exchange factor 38 (ARHGEF38) is related to tumor cell polarization and is frequently expressed in PCa. Microarray data of PCa were downloaded from GEO and TCGA databases. A total of 243 DEGs were screened, of which, 32 genes were upregulated. The results of enrichment analysis showed the participation of these DEGs in the tumor cell metastasis pathway. ARHGEF38 was significantly up-regulated in the four most prevalent cancers worldwide (p < 0.05), and its expression was higher in the tumor samples with higher Gleason score (GS). IHC, qRT-PCR, and western-blot analyses showed the higher expression of ARHGEF38 in PCa than benign prostatic hyperplasia (BPH). In addition, IHC results demonstrated a higher expression of ARHGEF38 in high-grade PCa than the low-grade PCa.

Identification of MFI2-AS1, a Novel Pivotal lncRNA for Prognosis of Stage III/IV Colorectal Cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) have been shown to play pivotal role in pathogenesis and prognosis of cancers. Identification of novel clinical biomarkers in advanced stage colorectal cancer (CRC) is warranted. AIMS: To identify potential lncRNAs associated with progression of stage III/IV CRC and illuminate regulatory mechanisms. METHODS: Differentially expressed lncRNAs, mRNAs and miRNAs (DElncRNAs, DEmRNAs, and DEmiRNAs) were extracted between stage III/IV CRC and normal tissues. We used DEGs to construct a ceRNA network and analyzed correlations between key lncRNAs and overall survivals (OS) of stage III/IV CRC patients. Weighted gene co-expression network analysis (WGCNA) was applied to a pivotal lncRNA. We conducted functional enrichment analysis on target genes and constructed lncRNA-TF-mRNA network by overlapping mRNAs co-expressed with the key lncRNA and target genes of transcriptional factors (TFs). RESULTS: A total of 26 DElncRNAs, 398 DEmiRNAs, 2155 DEmRNAs were identified. A ceRNA network was constructed with 16 lncRNAs, 20 miRNAs, and 59 mRNAs, in which MFI2-AS1 exhibited promising diagnostic efficiency. (AUC was 0.938.) MFI2-AS1 was negatively correlated to OS of stage III/IV CRC patients (P value < 0.05). KEGG analysis showed potential mRNA targets of MFI2-AS1 mainly involved in cell cycle and cytokine-cytokine receptor interaction. We identified 17 potential TFs of MFI2-AS1 and built a lncRNA-TF-mRNA network. CONCLUSION: Our study provides novel insights into lncRNAs associated regulatory networks and reveals a promising lncRNA biomarker, MFI2-AS1, as an independent prognostic indicator and potential therapeutic target for CRC.

A seven-DNA methylation signature as a novel prognostic biomarker in breast cancer.

BACKGROUND: Breast cancer (BC) is a common malignant tumor and its incidence and mortality rates are ranked first among female cancers. So far, there has been no effective biomarkers for BC prognosis. METHODS: The DNA methylation data of BC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, and Functional ANnoTation of The Mammalian Genome databases. The RNA-Seq data and clinical information of patients were downloaded from TCGA. R packages edgeR and minfi were used for differentially methylated genes (DMGs) screening. Then, the DMGs were collected for gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis by the online tool database for annotation, visualization and integrated discovery (DAVID) and Reactome. Cox regression analysis was used to screen candidate differentially methylated sites (DMSs) for BC prognosis. Logrank test was used to explore the correlation between DMSs and survival time. Correlation analysis was used to investigate the correlation between DNA methylation and gene expression. RESULTS: We identified 276 DMGs which contained 1454 DMSs in those three datasets. Also, six DMGs that contained seven DMSs were identified by Cox regression analysis. Interestingly, their expression levels were negatively correlated with the DNA methylation level and not affected by age, subtypes, or tumor stages. CONCLUSIONS: We proposed that these seven differentially DNA methylation sites can be used as a novel prognostic biomarker for BC area under curve (AUC) = 0.74), which may facilitate research and benefit the clinical treatment of BC.

Multi-omics analysis reveals epithelial-mesenchymal transition-related gene FOXM1 as a novel prognostic biomarker in clear cell renal carcinoma.

Identification of novel clinical biomarker in clear cell renal carcinoma (ccRCC) is warranted. Integrating transcriptome (n=1669), DNA methylation (n=577) and copy number data (n=832), we developed a method to identify driver biomarkers by analyzing the omics-level dynamics of Epithelial-Mesenchymal Transition (EMT)-related genes in ccRCC. We first identified 504 expression dynamic changed genes involved in ccRCC-associated key pathways such as EMT, cell cycle, EGFR and PI3K/AKT signaling. Further analysis identified 229 (90 gene promoters) aberrant expression quantitative trait methylation (eQTM) and 256 genes with expression quantitative trait copy number (eQTCN) alterations. Among them, FOXM1 was affected by both eQTM and eQTCN. FOXM1 copy number amplification (115/500, 23% of patients), occurred in an amplified peak in chromosome 12q13.3, was enriched in late-stage ccRCC samples and was associated with worse survival. FOXM1-overexpressed pT3 patients with distant metastasis showed ~25% shorter overall survival in both training (log-rank P=0.006) and validation (log-rank P=0.018) cohorts. The eQTM-gene hybrid signature (cg00044170 and FOXM1), superior to either gene expression or DNA methylation alone, showed great potential in diagnosing localized ccRCC in training (area under curve = 0.958) and validation datasets. FOXM1 could be a novel prognostic biomarker and shed light for early diagnosis at molecular level in ccRCC.

Transmembrane Channel-Like 5 (TMC5) promotes prostate cancer cell proliferation through cell cycle regulation.

BACKGROUND: In this study, we aimed to investigate the biological functions of Transmembrane Channel-Like 5 (TMC5) by bioinformatics and molecular biology methods in prostate cancer (PCa). METHODS: We assessed the mRNA expression level of TMC5 in PCa with public database the Cancer Genome Atlas (TCGA) and Oncomine. The biological functions were demonstrated by bioinformatics methods and siRNA mediated knockdown experiments. Reverse transcription polymerase chain reaction (RT-PCR), immunohistochemical (IHC) experiments and microarray analysis were performed to confirm the results. RESULTS: TMC5 expression level was significantly up-regulated in 4 independent PCa cohorts compared to normal group. Moreover, TMC5 has higher diagnostic efficiency than PSA-KLK3 (AUC (Area Under Curve) = 0.772, P < 0.001). The high expression of TMC5 was associated with clinical Gleason score, prostate-specific antigen (PSA) level, androgen receptor (AR) activity score and the genes which were known frequently mutated in PCa progression (P < 0.05). Functionally, Gene Otology (GO) analysis suggested that TMC5 was related to cell development; TMC5 knockdown significantly inhibited PCa cells proliferation by arresting cell cycle at G1 phase. Drug sensitivity experiments showed TMC5 knockdown significantly enhanced cells sensitivity to 5-Fluorouracil. Microarray analysis showed TMC5 knockdown significantly inhibited cell cycle and tumor progression. CONCLUSION: Our findings revealed that TMC5 promoted PCa cell proliferation through cell cycle regulation and could be a powerful and hopeful target for PCa treatment.

SLCO4C1 promoter methylation is a potential biomarker for prognosis associated with biochemical recurrence-free survival after radical prostatectomy.

BACKGROUND: Prostate cancer (PC) is a commonly diagnosed malignancy in males, especially in the western hemisphere. The extensive use of multiple biomarkers plays an important role in the diagnosis and prognosis of PC. However, the accuracy of biomarkers for PC prognosis needs to be urgently improved. This study aimed to identify a novel prognostic biomarker for PC. MATERIALS AND METHODS: Differentially methylated CpG sites were identified from the GSE76938 dataset ( https://www.ncbi.nlm.nih.gov/geo/ ) using R software version 3.1.4. Four significant CpG sites on the SLCO4C1 gene were found to be closely associated with prognosis in PC. Data downloaded from The Cancer Genome Atlas (TCGA) were used for validation. Co-expression and functional enrichment analyses were used to explore the roles of SLCO4C1 in molecular functions, biological processes and cellular components. Total RNA extraction and qRT-PCR were used to reveal the difference in SLCO4C1 expression between tumour and normal tissues. Bisulfite amplicon sequencing (BSAS) was used to identify methylation levels at the CpG sites. RESULTS: In the GSE76938 cohort, 10,206 CpG sites were identified to be differentially methylated in tumour versus normal prostate tissues. Among the CpG sites, four sites (cg06480736, cg19774478, cg19788741 and cg22149516) located in the promotor region (TSS200-1500) of SLCO4C1 were found to be significantly hypermethylated in tumour tissues. The results were validated in an independent dataset (TCGA PRAD cohort). In the cohort from TCGA, SLCO4C1 expression was negatively correlated with methylation levels at the four sites. The results of qRT-PCR validated that tumour tissues had a relatively lower expression of SLCO4C1. Bisulfite amplicon sequencing (BSAS) further confirmed a higher methylation level at the SLCO4C1 promoter in tumour tissues. SLCO4C1 (cg06480736, cg19774478, cg19788741 and cg22149516) was identified as a significant promising biomarker for biochemical recurrence-free survival in Kaplan-Meier analysis (P < 0.01) and univariate Cox proportional hazards analysis: cg06480736 (HR 15.914, P < 0.001), cg19774478 (HR 9.001, P < 0.001), cg19788741 (HR 10.759, P = 0.003) and cg22149516 (HR 17.144, P = 0.006). However, three sites, namely, cg06480736 (HR 1.809, P = 0.049), cg19774478 (HR 1.903, P = 0.041) and cg22149516 (HR 2.316, P = 0.008), were confirmed in multivariate analysis. CONCLUSIONS: SLCO4C1 promoter methylation, including that at three CpG sites, namely, cg06480736, cg19774478 and cg22149516, is a potential biomarker for risk stratification and might offer significantly relevant prognostic information for PC patients after radical prostatectomy.

Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data.

Gastric cancer (GC) is a prevalent malignant cancer of digestive system, identification of novel diagnostic and prognostic biomarkers for GC is urgently demanded. The aim of this study was to determine potential long noncoding RNAs (lncRNAs) associated with the pathogenesis and prognosis of GC. Raw noncoding RNA microarray data (GSE53137, GSE70880, and GSE99417) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by The Cancer Genome Atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway, survival analysis were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. In total of 246 integrated differential genes including 15 lncRNAs and 241 messenger RNAs (mRNAs) were obtained after intersections of differential genes between GEO and TCGA database. ceRNA network comprised of three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs) and 72 mRNAs. Functional analysis revealed that three lncRNAs were mainly dominated in cell cycle and cellular senescence. Survival analysis showed that HMGA1P4 was statistically related to the overall survival rate. For the first time, we identified that HMGA1P4, a target of miR-301b/miR-508, is involved in cell cycle and senescence process by regulating CCNA2 in GC. Finally, the expression levels of three lncRNAs were validated to be upregulated in GC tissues. Thus, three lncRNAs including UCA1, HOTTIP, and HMGA1P4 may contribute to GC development and their potential functions might be associated with the prognosis of GC.

FOXS1 is regulated by GLI1 and miR-125a-5p and promotes cell proliferation and EMT in gastric cancer.

Gastric cancer (GC) is the fourth most common malignant neoplasm and the second leading cause of cancer death. Identification of key molecular signaling pathways involved in gastric carcinogenesis and progression facilitates early GC diagnosis and the development of targeted therapies for advanced GC patients. Emerging evidence has revealed a close correlation between forkhead box (FOX) proteins and cancer development. However, the prognostic significance of forkhead box S1 (FOXS1) in patients with GC and the function of FOXS1 in GC progression remain undefined. In this study, we found that upregulation of FOXS1 was frequently detected in GC tissues and strongly correlated with an aggressive phenotype and poor prognosis. Functional assays confirmed that FOXS1 knockdown suppressed cell proliferation and colony numbers, with induction of cell arrest in the G0/G1 phase of the cell cycle, whereas forced expression of FOXS1 had the opposite effect. Additionally, forced expression of FOXS1 accelerated tumor growth in vivo and increased cell migration and invasion through promoting epithelial-mesenchymal transition (EMT) both in vitro and in vivo. Mechanistically, the core promoter region of FOXS1 was identified at nucleotides -660~ +1, and NFKB1 indirectly bind the motif on FOXS1 promoters and inhibit FOXS1 expression. Gene set enrichment analysis revealed that the FOXS1 gene was most abundantly enriched in the hedgehog signaling pathway and that GLI1 expression was significantly correlated with FOXS1 expression in GC. GLI1 directly bound to the promoter motif of FOXS1 and significantly decreased FOXS1 expression. Finally, we found that miR-125a-5p repressed FOXS1 expression at the translational level by binding to the 3' untranslated region (UTR) of FOXS1. Together, these results suggest that FOXS1 can promote GC development and could be exploited as a diagnostic and prognostic biomarker for GC.

ZNF154 is a promising diagnosis biomarker and predicts biochemical recurrence in prostate cancer.

OBJECTIVE: To screen the methylated genes for early diagnosis and biochemical recurrence (BCR) prediction in prostate cancer (PCa) patients. METHODS: Differentially methylated CpG sites (DMCs) of PCa were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Combined with TCGA RNA sequencing data and clinical information, the DMCs associated genes with different expression and related to BCR were selected as candidate genes. Then, the expression level of the best candidate gene ZNF154 was validated by quantitative real-time polymerase chain reaction (qRT-PCR). Finally, the prognosis potential of the hypermethylation gene ZNF154 was assessed by Kaplan-Meier, univariate and multivariate cox regression analysis. RESULTS: A total of 87 candidate genes were screened out. Compared to benign prostate (BP) tissues, ZNF154 has three hypermethylation sites (cg03234186, cg12506930, cg26465391) in the promoter region in PCa tissues. qRT-PCR results showed that ZNF154 expression level was reduced in PCa tissues than in BP tissues (P = 0.004). Besides, the ZNF154 methylation level was negatively correlated with mRNA expression (r = -0.766, P < 0.001), and was highly cancer-specific in PCa (area under the curves (AUCs) = 90.030%). In addition, Kaplan-Meier analysis showed ZNF154 methylation level was associated with BCR (P = 0.005), and ZNF154 could be an independent factor for BCR prediction in PCa by using univariate and multivariate cox regression analysis (P = 0.035, HR = 8.218). CONCLUSIONS: 87 PCa specific genes were obtained. Further analysis gave the evidence that ZNF154 can be used as a specific maker for PCa diagnosis. Hypermethylation level of ZNF154 lead to gene expression inhibition and function loss, which contribute to the development and poor outcomes in PCa. In addition, the mean methylation level of ZNF154 can be used as an independent risk factor to predict BCR.

A panel of 7 prognosis-related long non-coding RNAs to improve platinum-based chemoresistance prediction in ovarian cancer.

In order to study the role of long non-coding RNAs (lncRNAs) in predicting platinum-based chemoresistance in patients with high-grade serous ovarian carcinoma (HGS-OvCa), a=7-lncRNA signature was developed by analyzing 561 microarrays and 136 specimens from RNA-sequencing (RNA-seq) obtained from online databases [odds ratio (OR), 2.859; P<0.0001]. The upregulated lncRNAs (RP11-126K1.6, ZBED3-AS1, RP11-439E19.10 and RP11­348N5.7) and downregulated lncRNAs [RNF144A-AS1, growth arrest specific 5 (GAS5) and F11-AS1] exhibited high sensitivity and specificity in predicting chemoresistance in the Gene Expression Omnibus and the Cancer Genome Atlas (area under curve >0.8). The lncRNA signature was independent of clinical characteristics and 4 HGS-OvCa molecular subtypes. This signature was negatively associated with disease-free survival (n=47; log-rank, P<0.01). Furthermore, the expression of the 7 lncRNAs was consistent with microarray (GSE63885, GSE51373, GSE15372 and GSE9891) and RNA-seq data. In in vitro experiments, ZBED3-AS1, F11-AS1 and GAS5 were differentially expressed in cell lines that are known to be resistant and non-resistant to platinum-based drugs, which was consistent with the results in the present study. This lncRNA signature may be used as a prognostic marker for predicting resistance to platinum-based chemotherapeutics in HGS-OvCa. These findings may contribute to individualized therapies in patients with HGS-OvCa in the future.

Prediction of biomarkers of oral squamous cell carcinoma using microarray technology.

Microarray data is used to screen the genes of oral squamous cell carcinoma (OSCC). Microarray data of OSCC and normal tissues were downloaded from GEO database and analyzed with Benjamini-Hochberg (BH) method. Differentially expressed genes (DEGs) were then uploaded on DAVID database to process enrichment analysis. Target genes were finally chosen for verification experiment in vitro and in vivo. 78 DEGs were selected from 54676 genes, including 46 up- and 32 down- regulation. GO term showed that these genes were related to epidermal growth (biological processes), extracellular region (cellular components) and cytokines activity (molecular function). Protein network interaction demonstrated that OSCC was closely allied to the five key genes including CXCL10, IFI6, IFI27, ADAMTS2 and COL5A1, which was consistent with the RT-PCR data. High-expressed gene CXCL10 was chosen for further cell experiment, and the results indicated that CXCL10 can promote the proliferation, migration and invasion of normal cells and inhibited the cancer cells after si-RNA transfection. Moreover, it has been proven that CXCL10 was possibly related to the occurrence and development of OSCC. Understanding the regulation of OSCC expression will shed light on the screening of cancer biomarker.

Identification of prostate cancer LncRNAs by RNA-Seq.

PURPOSE: To identify prostate cancer lncRNAs using a pipeline proposed in this study, which is applicable for the identification of lncRNAs that are differentially expressed in prostate cancer tissues but have a negligible potential to encode proteins. MATERIALS AND METHODS: We used two publicly available RNA-Seq datasets from normal prostate tissue and prostate cancer. Putative lncRNAs were predicted using the biological technology, then specific lncRNAs of prostate cancer were found by differential expression analysis and co-expression network was constructed by the weighted gene co-expression network analysis. RESULTS: A total of 1,080 lncRNA transcripts were obtained in the RNA-Seq datasets. Three genes (PCA3, C20orf166-AS1 and RP11-267A15.1) showed a significant differential expression in the prostate cancer tissues, and were thus identified as prostate cancer specific lncRNAs. Brown and black modules had significant negative and positive correlations with prostate cancer, respectively. CONCLUSIONS: The pipeline proposed in this study is useful for the prediction of prostate cancer specific lncRNAs. Three genes (PCA3, C20orf166-AS1, and RP11-267A15.1) were identified to have a significant differential expression in prostate cancer tissues. However, there have been no published studies to demonstrate the specificity of RP11-267A15.1 in prostate cancer tissues. Thus, the results of this study can provide a new theoretic insight into the identification of prostate cancer specific genes.