A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma.

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.

Integrated analysis of transcription factors and targets co-expression profiles reveals reduced correlation between transcription factors and target genes in cancer.

Transcription factors are recognized as the key regulators of gene expression. However, the changes in the correlation of transcription factors and their target genes between normal and tumor tissues are usually ignored. In this research, we used mRNA expression profile data from The Cancer Genome Atlas which included 5726 samples across 11 major human cancers to perform co-expression analysis by the Pearson correlation coefficients. Then, integrating 81,357 pairs of transcription factors and target genes from transcription factors databases to find out the changes in the co-expression correlation of these gene pairs from normal to tumor tissues. Based on the changes in the number of co-expressed TF-TG pairs and changes in the level of co-expression, we found the generally reduced correlation between transcription factors and their target genes in cancer. Additionally, we screened out universal and specific transcription factors-target genes pairs which may significant influence particular cancer. Then, we obtained 423 cancer cell line expression profiles from Broad Institute Cancer Cell Line Encyclopedia to verify our results. Some of these pairs like XRCC5-XRCC6 have been reported to involve in multiple cancers, while pairs like IRF1-PSMB9 without any previous articles related to tumor but involve in the biological processes of cancer, which are of great potential to be therapeutic targets. Our research may provide insights to better understand the tumor development mechanisms and find potential therapeutic targets.

Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency.

In the past decade, treatments for tumors have made remarkable progress, such as the successful clinical application of targeted therapies. Nowadays, targeted therapies are based primarily on the detection of mutations, and next-generation sequencing (NGS) plays an important role in relevant clinical research. The mutation frequency is a major problem in tumor mutation detection and increasing sequencing depth is a widely used method to improve mutation calling performance. Therefore, it is necessary to evaluate the effect of different sequencing depth and mutation frequency as well as mutation calling tools. In this study, Strelka2 and Mutect2 tools were used in detecting the performance of 30 combinations of sequencing depth and mutation frequency. Results showed that the precision rate kept greater than 95% in most of the samples. Generally, for higher mutation frequency (≥20%), sequencing depth ≥200X is sufficient for calling 95% mutations; for lower mutation frequency (≤10%), we recommend improving experimental method rather than increasing sequencing depth. Besides, according to our results, although Strelka2 and Mutect2 performed similarly, the former performed slightly better than the latter one at higher mutation frequency (≥20%), while Mutect2 performed better when the mutation frequency was lower than 10%. Besides, Strelka2 was 17 to 22 times faster than Mutect2 on average. Our research will provide a useful and comprehensive guideline for clinical genomic researches on somatic mutation identification through systematic performance comparison among different sequencing depths and mutation frequency.

Comprehensive analysis of long non-coding RNAs and mRNAs in skeletal muscle of diabetic Goto-Kakizaki rats during the early stage of type 2 diabetes.

Skeletal muscle long non-coding RNAs (lncRNAs) were reported to be involved in the development of type 2 diabetes (T2D). However, little is known about the mechanism of skeletal muscle lncRNAs on hyperglycemia of diabetic Goto-Kakizaki (GK) rats at the age of 3 and 4 weeks. To elucidate this, we used RNA-sequencing to profile the skeletal muscle transcriptomes including lncRNAs and mRNAs, in diabetic GK and control Wistar rats at the age of 3 and 4 weeks. In total, there were 438 differentially expressed mRNAs (DEGs) and 401 differentially expressed lncRNAs (DELs) in skeletal muscle of 3-week-old GK rats compared with age-matched Wistar rats, and 1000 DEGs and 726 DELs between GK rats and Wistar rats at 4 weeks of age. The protein-protein interaction analysis of overlapping DEGs between 3 and 4 weeks, the correlation analysis of DELs and DEGs, as well as the prediction of target DEGs of DELs showed that these DEGs (Pdk4, Stc2, Il15, Fbxw7 and Ucp3) might play key roles in hyperglycemia, glucose intolerance, and increased fatty acid oxidation. Considering the corresponding co-expressed DELs with high correlation coefficients or targeted DELs of these DEGs, our study indicated that these dysregulated lncRNA-mRNA pairs (NONRATG017315.2-Pdk4, NONRATG003318.2-Stc2, NONRATG011882.2-Il15, NONRATG013497.2-Fbxw7, MSTRG.1662-Ucp3) might be related to above biological processes in GK rats at the age of 3 and 4 weeks. Our study could provide more comprehensive knowledge of mRNAs and lncRNAs in skeletal muscle of GK rats at 3 and 4 weeks of age. And our study may provide deeper understanding of the underlying mechanism in T2D of GK rats at the age of 3 and 4 weeks.

Systematic comparison of germline variant calling pipelines cross multiple next-generation sequencers.

The development and innovation of next generation sequencing (NGS) and the subsequent analysis tools have gain popularity in scientific researches and clinical diagnostic applications. Hence, a systematic comparison of the sequencing platforms and variant calling pipelines could provide significant guidance to NGS-based scientific and clinical genomics. In this study, we compared the performance, concordance and operating efficiency of 27 combinations of sequencing platforms and variant calling pipelines, testing three variant calling pipelines-Genome Analysis Tool Kit HaplotypeCaller, Strelka2 and Samtools-Varscan2 for nine data sets for the NA12878 genome sequenced by different platforms including BGISEQ500, MGISEQ2000, HiSeq4000, NovaSeq and HiSeq Xten. For the variants calling performance of 12 combinations in WES datasets, all combinations displayed good performance in calling SNPs, with their F-scores entirely higher than 0.96, and their performance in calling INDELs varies from 0.75 to 0.91. And all 15 combinations in WGS datasets also manifested good performance, with F-scores in calling SNPs were entirely higher than 0.975 and their performance in calling INDELs varies from 0.71 to 0.93. All of these combinations manifested high concordance in variant identification, while the divergence of variants identification in WGS datasets were larger than that in WES datasets. We also down-sampled the original WES and WGS datasets at a series of gradient coverage across multiple platforms, then the variants calling period consumed by the three pipelines at each coverage were counted, respectively. For the GIAB datasets on both BGI and Illumina platforms, Strelka2 manifested its ultra-performance in detecting accuracy and processing efficiency compared with other two pipelines on each sequencing platform, which was recommended in the further promotion and application of next generation sequencing technology. The results of our researches will provide useful and comprehensive guidelines for personal or organizational researchers in reliable and consistent variants identification.

Transcriptome Changes of Skeletal Muscle RNA-Seq Speculates the Mechanism of Postprandial Hyperglycemia in Diabetic Goto-Kakizaki Rats During the Early Stage of T2D.

To address how skeletal muscle contributes to postprandial hyperglycemia, we performed skeletal muscle transcriptome analysis of diabetic Goto-Kakizaki (GK) and control Wistar rats by RNA sequencing (RNA-Seq). We obtained 600 and 1785 differentially expressed genes in GK rats compared to those Wistar rats at three and four weeks of age, respectively. Specifically, Tbc1d4, involved in glucose uptake, was significantly downregulated in the skeletal muscle of GK aged both three and four weeks compared to those of age-matched Wistar rats. Pdk4, related to glucose uptake and oxidation, was significantly upregulated in the skeletal muscle of GK aged both three and four weeks compared to that of age-matched Wistar rats. Genes (Acadl, Acsl1 and Fabp4) implicated in fatty acid oxidation were significantly upregulated in the skeletal muscle of GK aged four weeks compared to those of age-matched Wistar rats. The overexpression or knockout of Tbc1d4, Pdk4, Acadl, Acsl1 and Fabp4 has been reported to change glucose uptake and fatty acid oxidation directly in rodents. By taking the results of previous studies into consideration, we speculated that dysregulation of key dysregulated genes (Tbc1d4, Pdk4, Acadl, Acsl1 and Fabp4) may lead to a decrease in glucose uptake and oxidation, and an increase in fatty acid oxidation in GK skeletal muscle at three and four weeks, which may, in turn, contribute to postprandial hyperglycemia. Our research revealed transcriptome changes in GK skeletal muscle at three and four weeks. Tbc1d4, Acadl, Acsl1 and Fabp4 were found to be associated with early diabetes in GK rats for the first time, which may provide a new scope for pathogenesis of postprandial hyperglycemia.

Integrative Analysis Reveals Comprehensive Altered Metabolic Genes Linking with Tumor Epigenetics Modification in Pan-Cancer.

BACKGROUND: Cancer cells undergo various rewiring of metabolism and dysfunction of epigenetic modification to support their biosynthetic needs. Although the major features of metabolic reprogramming have been elucidated, the global metabolic genes linking epigenetics were overlooked in pan-cancer. OBJECTIVES: Identifying the critical metabolic signatures with differential expressions which contributes to the epigenetic alternations across cancer types is an urgent issue for providing the potential targets for cancer therapy. METHOD: The differential gene expression and DNA methylation were analyzed by using the 5726 samples data from the Cancer Genome Atlas (TCGA). RESULTS: Firstly, we analyzed the differential expression of metabolic genes and found that cancer underwent overall metabolism reprogramming, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Secondly, the regulatory network of histone acetylation and DNA methylation according to altered expression of metabolism genes was summarized in our results. Then, the survival analysis showed that high expression of DNMT3B had a poorer overall survival in 5 cancer types. Integrative altered methylation and expression revealed specific genes influenced by DNMT3B through DNA methylation across cancers. These genes do not overlap across various cancer types and are involved in different function annotations depending on the tissues, which indicated DNMT3B might influence DNA methylation in tissue specificity. CONCLUSIONS: Our research clarifies some key metabolic genes, ACLY, SLC2A1, KAT2A, and DNMT3B, which are most disordered and indirectly contribute to the dysfunction of histone acetylation and DNA methylation in cancer. We also found some potential genes in different cancer types influenced by DNMT3B. Our study highlights possible epigenetic disorders resulting from the deregulation of metabolic genes in pan-cancer and provides potential therapy in the clinical treatment of human cancer.

Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer.

BACKGROUND: Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues. OBJECTIVES: The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor. MATERIALS AND METHODS: 5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively. RESULTS: We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers. CONCLUSIONS: This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

Identification and Validation of Circulating MicroRNA Signatures for Breast Cancer Early Detection Based on Large Scale Tissue-Derived Data.

PURPOSE: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. METHODS: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. RESULTS: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. CONCLUSION: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.

Evaluation of fatty proportion in fatty liver using least squares method with constraints.

Backscatter and attenuation parameters are not easily measured in clinical applications due to tissue inhomogeneity in the region of interest (ROI). A least squares method(LSM) that fits the echo signal power spectra from a ROI to a 3-parameter tissue model was used to get attenuation coefficient imaging in fatty liver. Since fat's attenuation value is higher than normal liver parenchyma, a reasonable threshold was chosen to evaluate the fatty proportion in fatty liver. Experimental results using clinical data of fatty liver illustrate that the least squares method can get accurate attenuation estimates. It is proved that the attenuation values have a positive correlation with the fatty proportion, which can be used to evaluate the syndrome of fatty liver.

Galactosylated poly(ethylene glycol) methacrylate-st-3-guanidinopropyl methacrylamide copolymers as siRNA carriers for inhibiting Survivin expression in vitro and in vivo.

In this report, galactosylated poly(ethylene glycol) methacrylate-st-3-guanidinopropyl methacrylamide copolymers (galactosylated PEGMA-st-GPMA, GGP) are developed as siRNA carriers to inhibit Survivin mRNA expression. GGPs are combined with Survivin siRNAs to form siRNA/GGP polyplexes. The polyplexes particles were examined by a dynamic light scattering. It showed that GGP copolymers could condense siRNA to form particles with diameter from 128 to 423 nm and zeta potential value in the range from +2.4 to +14.9 mV at various charge ratios (N/P). The MTT assay data of siRNA/GGP polyplexes on human hepatocellular liver carcinoma cells (HepG2) and human cervix epithelial carcinoma cells (HeLa) indicated that GGP copolymer had better cell viabilities than polyethyleimine (PEI). The transfection of siRNA/GGP polyplexes was detected by real-time quantitative PCR (RT-qPCR) in HepG2 cell line. We found that the siRNA/GGP polyplexes could effectively silence Survivin mRNA expression in the serum-free media (p < 0.01). In the presence of 10% serum medium, the Survivin mRNA expressed has significant difference between siRNA/GGP polyplexes and blank (p < 0.05). The galactose competition assay showed that galactosylated PEGMA-st-GPMA (GGP) may provide the targeting to HepG2 cells mediating by asialoglycoproteins receptors (ASGP-R). Furthermore, Survivin siRNA/GGP polyplexes could significantly (p < 0.01) inhibit both HepG2 tumor growth and Survivin protein expression in vivo studies in a xenograft mouse model.