Detecting cancer outlier genes with potential rearrangement using gene expression data and biological networks

Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum

While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.

Robust Principal Component Analysis Based On Hypergraph Regularization for Sample Clustering and Co-Characteristic Gene Selection

Extracting genes involved in cancer lesions from gene expression data is critical for cancer research and drug development. The method of feature selection has attracted much attention in the field of bioinformatics. Principal Component Analysis (PCA) is a widely used method for learning low-dimensional representation. Some variants of PCA have been proposed to improve the robustness and sparsity of the algorithm. However, the existing methods ignore the high-order relationships between data. In this paper, a new model named Robust Principal Component Analysis via Hypergraph Regularization (HRPCA) is proposed. In detail, HRPCA utilizes L2,1-norm to reduce the effect of outliers and make data sufficiently row-sparse. And the hypergraph regularization is introduced to consider the complex relationship among data. Important information hidden in the data are mined, and this method ensures the accuracy of the resulting data relationship information. Extensive experiments on multi-view biological data demonstrate that the feasible and effective of the proposed approach.

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.

Fatty acid-binding protein 5 predicts poor prognosis in patients with uveal melanoma

Fatty acid-binding protein 5 (FABP5), which participates in mediating the biological properties of tumor cells, has been recognized in several neoplasms. The present study aims to investigate FABP5 transcriptional expression profiles, reveal its underlying biological interaction networks and define its prognostic value in uveal melanoma (UVM). A total of 80 patients with UVM and their RNA-sequence data, available from The Cancer Genome Atlas (TCGA) database, was analyzed. A differential transcriptional expression profile was obtained from TCGA and the Oncomine databases. The survival benefits were analyzed using the Kaplan-Meier method and log-rank test. The correlation between FABP5 expression and immune infiltration level was analyzed using the Tumor Immune Estimation Resource database. Functional enrichment analyses using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and signaling hallmarks were utilized to describe the biological process, molecular functions, cellular component and significantly involved pathways. The elevated transcriptional expression of FABP5 was significantly associated with shorter overall survival (OS) and worse progression-free survival (PFS) times in patients with UVM (P<0.001). Moreover, FABP5 expression was significantly and positively correlated with tumor purity and CD8⁺ T cells and was negatively correlated with the infiltrating levels of CD4⁺ T cells and neutrophils. Gene Set Enrichment Analysis was performed to obtain 100 significantly associated genes of FABP5 and FABP5 was found to be critical in several hallmark pathways, including allograft rejection, complement, interleukin-6/Janus kinase-STAT3 signaling, interferon γ response, inflammatory response and tumor necrosis factor α signaling via NFκB. The present study is the first to demonstrate that FABP5 expression was positively associated with progression-associated clinicopathological factors and poor prognosis in UVM, which suggests its likely function as an oncogene and prognostic marker in patients with UVM.

Detection and analysis of stable and flexible genes towards a genome signature framework in cancer

Comparison and detection of stable cancer genes across cancer types is of interest. The gene expression data of 6 different cancer types (colon, breast, lung, ovarian, brain and renal) and a control group from The Cancer Genome Atlas (TCGA) database were used in this study. The comparison of gene expression data together with the calculation standard deviations of such data was completed using a statistical model for the detection of stable genes. Genes having similar expression (referred as flexible genes) pattern to the control group in four out of six cancer types are PATE, NEUROD4 and TRAFD1. Moreover, 13 genes showed low difference compared to the control group with low standard deviation across cancer types (referred as stable genes). Among them, genes GDF2, KCNT1 and RNF151 showed consistent low expression while ODF4, OR5I1, MYOG and OR2B11 showed consistent high expression. Thus, the detection and analysis of stable and flexible cancer genes help towards the design and development of a framework (outline) for specific genome signature (biomarker) in cancer.

Principal Component Analysis Based on Graph Laplacian and Double Sparse Constraints for Feature Selection and Sample Clustering on Multi-View Data

Principal component analysis (PCA) is a widely used method for evaluating low-dimensional data. Some variants of PCA have been proposed to improve the interpretation of the principal components (PCs). One of the most common methods is sparse PCA which aims at finding a sparse basis to improve the interpretability over the dense basis of PCA. However, the performances of these improved methods are still far from satisfactory because the data still contain redundant PCs. In this paper, a novel method called PCA based on graph Laplacian and double sparse constraints (GDSPCA) is proposed to improve the interpretation of the PCs and consider the internal geometry of the data. In detail, GDSPCA utilizes L2,1-norm and L1-norm regularization terms simultaneously to enforce the matrix to be sparse by filtering redundant and irrelative PCs, where the L2,1-norm regularization term can produce row sparsity, while the L1-norm regularization term can enforce element sparsity. This way, we can make a better interpretation of the new PCs in low-dimensional subspace. Meanwhile, the method of GDSPCA integrates graph Laplacian into PCA to explore the geometric structure hidden in the data. A simple and effective optimization solution is provided. Extensive experiments on multi-view biological data demonstrate the feasibility and effectiveness of the proposed approach.

Altered epidermal fatty acid-binding protein expression in hepatocellular carcinoma predicts unfavorable outcomes

Objective

Hepatocellular carcinoma (HCC) is a rapidly proliferating malignancy that requires large amounts of fatty acids to synthesize cellular membranes and provide energy. Epidermal fatty acid-binding protein (EFABP) is uniquely expressed in epidermal cells, but its role and expression in HCC are not clear.

Subjects and methods

A total of 804 HCC specimens were collected to construct a tissue microarray (TMA) and for immunohistochemistry (IHC) analysis. The relationship between EFABP expression and clinical features of patients with HCC was analyzed.

Results

The EFABP IHC score for HCC tissue was 0.76±0.69, being significantly higher than that for matched nontumorous tissue (0.48±0.55; P<0.001). Using the median IHC score (ie, 0.8) in the tumorous tissue, a high level of EFABP expression was found in 57.3% (461/804) of the cases. Patients with HCC displaying high EFABP expression had poorer tumor differentiation (P=0.029), more vascular invasion (P=0.006), and a higher proportion of late TNM stage disease (P=0.042). Kaplan-Meier analysis revealed that the patients with high EFABP expression had significantly worse outcomes in terms of overall survival (P=0.003), worse disease-free survival (P=0.021), and a higher probability of recurrence (P=0.014). Multivariate analysis indicated that EFABP expression was an independent prognostic variable for overall survival (P=0.021) and disease-free survival (P=0.044). For HCC recurrence, only vascular invasion (P=0.020) and EFABP expression (P=0.026) were independent risk factors.

Conclusion

Our data revealed that EFABP expression was increased in HCC samples. High EFABP expression was correlated with shorter survival times in patients with HCC and served as an independent factor for worse outcomes. Our study therefore provides a promising bio-marker for the prognostic prediction of HCC and a potential therapeutic target for the disease.

Fatty acid-binding protein 5 function in hepatocellular carcinoma through induction of epithelial-mesenchymal transition

Hepatocellular carcinoma (HCC) is a highly prevalent cancer with poor prognosis. The correlation between overexpression of fatty acid-binding protein 5 (FABP5) and malignant potential of tumor growth and metastasis in several cancers has been previously reported. However, the correlation between FABP5 expression and HCC malignant behavior remains unknown. We compared FABP5 expression and patient characteristics in paired HCC and adjacent noncancerous liver tissues from 243 patients who underwent surgical resection of primary HCC. Cell proliferation, invasion, and migration assays were performed in HCC cell lines overexpressing FABP5 or downregulated for FABP5. Tumor growths were monitored in xenograft model, and liver and lung metastasis models were established. In the 243 HCC patients, FABP5-positive staining (n = 139/243, 57.2%) was associated with poor prognosis and recurrence (P < 0.0001) and showed positive correlation with distant metastasis, tumor size and vascular invasion (P < 0.05). Cell proliferation, invasion, and migration in vitro were enhanced by upregulation of FABP5 and decreased by downregulation of FABP5 in HCC cell lines. Similar results in tumor formation and metastasis were obtained through in vivo analyses. PCR array results revealed upregulation of SNAI1 in FABP5-overexpressing HepG2 cells. Western blot analysis showed significantly increased expression of E-cadherin and ZO-1 and decreased SNAI1 expression and nuclear translocation of β-catenin by knockdown of FABP5. We revealed a significant role for FABP5 in HCC progression and metastasis through the induction of epithelial-to-mesenchymal transition. FABP5 may be a potential novel prognostic biomarker and new therapeutic target for HCC.

Serine protease inhibitor Kazal type 1 promotes epithelial-mesenchymal transition through EGFR signaling pathway in prostate cancer

BACKGROUND

Overexpression of serine protease inhibitor Kazal type 1 (SPINK1) defines an aggressive molecular subtype of ETS fusion-negative prostate cancer (PCa) patients in western countries. However, how SPINK1 contributes to PCa invasion and metastasis is largely unknown.

METHODS

Fluorescence in situ hybridization and immunohistochemistry were utilized to detect ERG rearrangement, SPINK1 expression, and EGFR aberrations in a cohort of 211 PCa patients with radical prostatectomy. Real-time quantitative PCR and Western blotting were used to study the transcript and protein expression levels. Cellular distribution of E-cadherin and vimentin were observed by immunofluorescence. Cellular function was evaluated by siRNA, transwell, and wound healing assay, respectively.

RESULTS

SPINK1-induced Epithelial-mesenchymal transition (EMT) in benign prostate RWPE cells, manifested by acquisition of mesenchymal morphology, alternation of EMT markers as well as migration and invasion capabilities. Knockdown of SPINK1 in 22RV1 PCa cells results in up-regulation of E-cadherin and down-regulation of vimentin. SPINK1-induced EMT is mediated by EGFR, in which MAPK/MEK/ERK pathway is mainly involved. Connective tissue growth factor (CTGF) might be an important down-stream molecule of SPINK1-EGFR axis. Clinically, SPINK1 and EGFR were significantly co-overexpressed in a cohort of Chinese PCa patients (n > 200). SPINK1 is an unfavorable prognostic factor in Chinese PCas (P = 0.025).

CONCLUSIONS

These findings suggest that SPINK1 promotes EMT through EGFR signaling pathway in PCa and SPINK1 could be a new prognostic marker in Chinese PCas.

Genetic ablation of the fatty acid-binding protein FABP5 suppresses HER2-induced mammary tumorigenesis

The fatty acid-binding protein FABP5 shuttles ligands from the cytosol to the nuclear receptor PPARβ/δ (encoded for by Pparδ), thereby enhancing the transcriptional activity of the receptor. This FABP5/PPARδ pathway is critical for induction of proliferation of breast carcinoma cells by activated epidermal growth factor receptor (EGFR). In this study, we show that FABP5 is highly upregulated in human breast cancers and we provide genetic evidence of the pathophysiologic significance of FABP5 in mammary tumorigenesis. Ectopic expression of FABP5 was found to be oncogenic in 3T3 fibroblasts where it augmented the ability of PPARδ to enhance cell proliferation, migration, and invasion. To determine whether FABP5 is essential for EGFR-induced mammary tumor growth, we interbred FABP5-null mice with MMTV-ErbB2/HER2 oncomice, which spontaneously develop mammary tumors. FABP5 ablation relieved activation of EGFR downstream effector signals, decreased expression of PPARδ target genes that drive cell proliferation, and suppressed mammary tumor development. Our findings establish that FABP5 is critical for mammary tumor development, rationalizing the development of FABP5 inhibitors as novel anticarcinogenic drugs.