Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.

Background: The initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma. Methods: The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry. Results: Patients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values. Conclusions: Our study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.

Prognostic biomarker identification and tumor classification in breast cancer patients by methylation and transcriptome analysis.

Breast cancer is one of the most common and heterogeneous malignancies. Although the prognosis of breast cancer has improved with the development of early screening, the mechanisms underlying tumorigenesis and progression remain incompletely understood. DNA methylation has been implicated in tumorigenesis and tumor development and, in the present study. we screened methylation-driven genes and explored their prognostic values in breast cancer. RNA-sequencing (RNA-Seq) transcriptome data and DNA methylation data of the TCGA-BRCA dataset were obtained from The Cancer Genome Atlas. Differentially expressed genes and differentially methylated genes were identified separately. The intersected 783 samples with both RNA-Seq data and DNA methylation data were selected for further analysis. Fifty-six methylation-driven genes were identified using the MethylMix r package and 10 prognosis methylation-driven genes (CDO1, CELF2, ITPAIPL1, KCNH8, PTK6, RAB25, RIC3, USP44, ZSCAN1 and ZSCAN23) were further screened by combined methylation and gene expression analysis. Based on the methylation data of the screened 10 methylation-driven genes, six subgroups were identified with the ConsensusClusterPlus r package. The protein levels of the 10 prognostic methylation-driven genes were detected by immunohistochemical experiments. Moreover, based on the RNA-Seq data, a signature calculating the risk score of each patient was developed with stepwise regression. The risk score and other clinical features (age and stage) were confirmed to be independent prognostic factors by univariate and multivariate Cox regression analyses. Finally, a prognostic nomogram incorporating all the significant factors was integrated to predict the 3-, 5- and 7-year overall survival. Taken together, the methylation-driven genes identified here may be potential biomarkers of breast cancer.

Long non-coding RNA KCNQ1OT1 promotes the progression of gastric cancer via the miR-145-5p/ARF6 axis.

BACKGROUND: Long non-coding RNA KCNQ1 opposite strand/antisense transcript one gene (KCNQ1OT1) has been reported to be involved in the progression of many types of human cancer, whereas its role in gastric cancer (GC) remains unknown. The present study aimed to investigate the role of KCNQ1OT1 in GC. METHODS: In total, 25 GC tissues and adjacent normal tissues were collected. The expression of KCNQ1OT1, miR-145-5p and ARF6 in GC tissues and cell lines was detected by quantitative reverse transcriptase-polymerase chain reaction or western blotting. Bioinformatics analysis and a dual luciferase reporter assay were performed to determine the relationship between KCNQ1OT1 and miR-145-5p or miR-145-5p and ARF6. Gain- and loss-of function of KCNQ1OT1 and miR-145-5p were achieved to confirm their roles in GC cells. Cell counting kit-8, colony formation and flow cytometry assays were used to evaluate cell viability, proliferation and apoptosis. A xenograft tumor model was established with BGC803 tumor cells transfected with sh-KCNQ1OT1 or empty vector to determine the role of LINC01089 in vivo. RESULTS: The expression levels of KCNQ1OT1 were markedly elevated in GC tissues and cells. Knockdown of KCNQ1OT1 inhibited GC tumor growth, reduced GC cell viability and colony formation, and induced GC cell apoptosis. The expression levels of miR-145-5p were significantly decreased in GC cells and correlated with the expression of KCNQ1OT1 in GC tumors. Moreover, KCNQ1OT1 directly binds with miR-145-5p, which is targeting ARF6. Knockdown of KCNQ1OT1 increased the expression levels of miR-145-5p. Inhibition of miR-145-5p increased the expression levels of KCNQ1OT1 and also attenuated the effects of knockdown of KCNQ1OT1 on the viability, proliferation and apoptosis of GC cells. In addition, overexpression of miR-145-5p reduced GC cell viability and colony formation and induced GC cell apoptosis, whereas overexpression of ARF6 attenuated the effects of overexpression of miR-145-5p on GC cell viability, colony formation and apoptosis. CONCLUSIONS: KCNQ1OT1 can promote GC progression through the miR-145-5p/ARF6 axis. KCNQ1OT1 may serve as a therapeutic target and a diagnostic biomarker of GC.

Activation of the LINC00242/miR-141/FOXC1 axis underpins the development of gastric cancer.

BACKGROUND: Long non-coding RNAs (LncRNAs) are a class of newly identified transcripts recognized as critical governors of gene expression during human carcinogenesis, whereas their tumor-suppressive or tumor-promoting effects on gastric cancer (GC) are required for further investigation. In the study, we identify the expression pattern of a novel lncRNA LINC00242 in GC and its possible permissive role in the development of GC. METHODS: The study included 68 pairs of GC and adjacent normal gastric tissue samples. The viability, migration, and invasion of cultured human GC cells HGC27 were evaluated by CCK-8 and Transwell chamber assays. In vitro tube formation of human brain microvascular endothelial cells (HBMVECs) in HGC27 cell coculture was detected. The regulatory network of LINC00242/miR-141/FOXC1 was verified using dual luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay. Subcutaneous xenografts of HGC27 cells were performed in nude mice. RESULTS: LINC00242 was highly expressed in GC tissues and cells and contributed to poor prognosis. LINC00242 knockdown inhibited HGC27 cell viability, migration and invasion, and tube formation of HBMVECs. LINC00242 interacted with miR-141 and positively regulated FOXC1, a target gene of miR-141. LINC00242 knockdown was partially lost in HGC27 cells upon miR-141 inhibition or FOXC1 overexpression. The tumor-promoting effect of LINC00242 on GC was demonstrated in nude mice. CONCLUSION: Taken together, the present study demonstrates the oncogenic role of the LINC00242/miR-141/FOXC1 axis in GC, highlighting a theoretical basis for GC treatment.