Impairment in acquisition of conditioned fear in people with depressive symptoms.

Background: Depression is one of the primary global public health issues, and there has been a dramatic increase in depression levels among young people over the past decade. The neuroplasticity theory of depression postulates that a malfunction in neural plasticity, which is responsible for learning, memory, and adaptive behavior, is the primary source of the disorder's clinical manifestations. Nevertheless, the impact of depression symptoms on associative learning remains underexplored. Methods: We used the differential fear conditioning paradigm to investigate the effects of depressive symptoms on fear acquisition and extinction learning. Skin conductance response (SCR) is an objective evaluation indicator, and ratings of nervousness, likeability, and unconditioned stimuli (US) expectancy are subjective evaluation indicators. In addition, we used associability generated by a computational reinforcement learning model to characterize the skin conductance response. Results: The findings indicate that individuals with depressive symptoms exhibited significant impairment in fear acquisition learning compared to those without depressive symptoms based on the results of the skin conductance response. Moreover, in the discrimination fear learning task, the skin conductance response was positively correlated with associability, as estimated by the hybrid model in the group without depressive symptoms. Additionally, the likeability rating scores improved post-extinction learning in the group without depressive symptoms, and no such increase was observed in the group with depressive symptoms. Conclusion: The study highlights that individuals with pronounced depressive symptoms exhibit impaired fear acquisition and extinction learning, suggesting a possible deficit in associative learning. Employing the hybrid model to analyze the learning process offers a deeper insight into the associative learning processes of humans, thus allowing for improved comprehension and treatment of these mental health problems.

Single-cell Landscape of Malignant Transition: Unraveling Cancer Cell-of-Origin and Heterogeneous Tissue Microenvironment.

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-ß production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

StemDriver: a knowledgebase of gene functions for hematopoietic stem cell fate determination.

StemDriver is a comprehensive knowledgebase dedicated to the functional annotation of genes participating in the determination of hematopoietic stem cell fate, available at http://biomedbdc.wchscu.cn/StemDriver/. By utilizing single-cell RNA sequencing data, StemDriver has successfully assembled a comprehensive lineage map of hematopoiesis, capturing the entire continuum from the initial formation of hematopoietic stem cells to the fully developed mature cells. Extensive exploration and characterization were conducted on gene expression features corresponding to each lineage commitment. At the current version, StemDriver integrates data from 42 studies, encompassing a diverse range of 14 tissue types spanning from the embryonic phase to adulthood. In order to ensure uniformity and reliability, all data undergo a standardized pipeline, which includes quality data pre-processing, cell type annotation, differential gene expression analysis, identification of gene categories correlated with differentiation, analysis of highly variable genes along pseudo-time, and exploration of gene expression regulatory networks. In total, StemDriver assessed the function of 23 839 genes for human samples and 29 533 genes for mouse samples. Simultaneously, StemDriver also provided users with reference datasets and models for cell annotation. We believe that StemDriver will offer valuable assistance to research focused on cellular development and hematopoiesis.

FusionNeoAntigen: a resource of fusion gene-specific neoantigens.

Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels) and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutation and are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on ∼43K fusion proteins of ∼16K in-frame fusion genes from FusionGDB2.0. FusionNeoAntigen provides fusion gene information, corresponding fusion protein sequences, fusion breakpoint peptide sequences, fusion gene-derived neoantigen prediction, virtual screening between fusion breakpoint peptides having potential fusion neoantigens and human leucocyte antigens (HLAs), fusion breakpoint RNA/protein sequences for developing vaccines, information on samples with fusion-specific neoantigen, potential CAR-T targetable cell-surface fusion proteins and literature curation. FusionNeoAntigen will help to develop fusion gene-based immunotherapies. We will report all potential fusion-specific neoantigens from all possible open reading frames of ∼120K human fusion genes in future versions.

Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer - bioinformatics study for a potential link to MMEJ.

Microhomology-mediated end joining (MMEJ), an error-prone DNA damage repair mechanism, frequently leads to chromosomal rearrangements due to its ability to engage in promiscuous end joining of genomic instability and also leads to increasing mutational load at the sequences flanking the breakpoints (BPs). In this study, we systematically investigated the homology sequences around the genomic breakpoint area of human fusion genes, which were formed by the chromosomal rearrangements initiated by DNA double-strand breakage. Since the RNA-seq data is the typical data set to check the fusion genes, for the known exon junction fusion breakpoints identified from RNA-seq data, we have to infer the high chance of genomic breakpoint regions. For this, we utilized the high feature importance score area calculated from our recently developed fusion BP prediction model, FusionAI and identified 151 K microhomologies among ~24 K fusion BPs in 20 K fusion genes. From our multiple bioinformatics studies, we found a relationship between sequence homologies and the immune system. This in-silico study will provide novel knowledge on the sequence homologies around the coded structural variants.

BarleyExpDB: an integrative gene expression database for barley.

BACKGROUND: RNA-sequencing (RNA-seq) has been widely used to study the dynamic expression patterns of transcribed genes, which can lead to new biological insights. However, processing and analyzing these huge amounts of histological data remains a great challenge for wet labs and field researchers who lack bioinformatics experience and computational resources. RESULTS: We present BarleyExpDB, an easy-to-operate, free, and web-accessible database that integrates transcriptional profiles of barley at different growth and developmental stages, tissues, and stress conditions, as well as differential expression of mutants and populations to build a platform for barley expression and visualization. The expression of a gene of interest can be easily queried by searching by known gene ID or sequence similarity. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Proteins Families Database, and Simple Modular Architecture Research Tool annotations. CONCLUSIONS: BarleyExpDB will serve as a valuable resource for the barley research community to leverage the vast publicly available RNA-seq datasets for functional genomics research and crop molecular breeding.

A novel integrated approach to predicting cancer immunotherapy efficacy.

Immunotherapies have revolutionized cancer treatment modalities; however, predicting clinical response accurately and reliably remains challenging. Neoantigen load is considered as a fundamental genetic determinant of therapeutic response. However, only a few predicted neoantigens are highly immunogenic, with little focus on intratumor heterogeneity (ITH) in the neoantigen landscape and its link with different features in the tumor microenvironment. To address this issue, we comprehensively characterized neoantigens arising from nonsynonymous mutations and gene fusions in lung cancer and melanoma. We developed a composite NEO2IS to characterize interplays between cancer and CD8+ T-cell populations. NEO2IS improved prediction accuracy of patient responses to immune-checkpoint blockades (ICBs). We found that TCR repertoire diversity was consistent with the neoantigen heterogeneity under evolutionary selections. Our defined neoantigen ITH score (NEOITHS) reflected infiltration degree of CD8+ T lymphocytes with different differentiation states and manifested the impact of negative selection pressure on CD8+ T-cell lineage heterogeneity or tumor ecosystem plasticity. We classified tumors into distinct immune subtypes and examined how neoantigen-T cells interactions affected disease progression and treatment response. Overall, our integrated framework helps profile neoantigen patterns that elicit T-cell immunoreactivity, enhance the understanding of evolving tumor-immune interplays and improve prediction of ICBs efficacy.

Integrated mRNA sequence optimization using deep learning.

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous gene such as severe acute respiratory syndrome coronavirus 2 spike to fit human cells remains a critical challenge. A new algorithm, iDRO (integrated deep-learning-based mRNA optimization), is developed to optimize multiple components of mRNA sequences based on given amino acid sequences of target protein. Considering the biological constraints, we divided iDRO into two steps: open reading frame (ORF) optimization and 5' untranslated region (UTR) and 3'UTR generation. In ORF optimization, BiLSTM-CRF (bidirectional long-short-term memory with conditional random field) is employed to determine the codon for each amino acid. In UTR generation, RNA-Bart (bidirectional auto-regressive transformer) is proposed to output the corresponding UTR. The results show that the optimized sequences of exogenous genes acquired the pattern of human endogenous gene sequence. In experimental validation, the mRNA sequence optimized by our method, compared with conventional method, shows higher protein expression. To the best of our knowledge, this is the first study by introducing deep-learning methods to integrated mRNA sequence optimization, and these results may contribute to the development of mRNA therapeutics.

The barley DIR gene family: An expanded gene family that is involved in stress responses.

Gene family expansion plays a central role in adaptive divergence and, ultimately, speciation is influenced by phenotypic diversity in different environments. Barley (Hordeum vulgare) is the fourth most important cereal crop in the world and is used for brewing purposes, animal feed, and human food. Systematic characterization of expanded gene families is instrumental in the research of the evolutionary history of barley and understanding of the molecular function of their gene products. A total of 31,750 conserved orthologous groups (OGs) were identified using eight genomes/subgenomes, of which 1,113 and 6,739 were rapidly expanded and contracted OGs in barley, respectively. Five expanded OGs containing 20 barley dirigent genes (HvDIRs) were identified. HvDIRs from the same OG were phylogenetically clustered with similar gene structure and domain organization. In particular, 7 and 5 HvDIRs from OG0000960 and OG0001516, respectively, contributed greatly to the expansion of the DIR-c subfamily. Tandem duplication was the driving force for the expansion of the barley DIR gene family. Nucleotide diversity and haplotype network analysis revealed that the expanded HvDIRs experienced severe bottleneck events during barley domestication, and can thus be considered as potential domestication-related candidate genes. The expression profile and co-expression network analysis revealed the critical roles of the expanded HvDIRs in various biological processes, especially in stress responses. HvDIR18, HvDIR19, and HvDIR63 could serve as excellent candidates for further functional genomics studies to improve the production of barley products. Our study revealed that the HvDIR family was significantly expanded in barley and might be involved in different developmental processes and stress responses. Thus, besides providing a framework for future functional genomics and metabolomics studies, this study also identified HvDIRs as candidates for use in improving barley crop resistance to biotic and abiotic stresses.

BGFD: an integrated multi-omics database of barley gene families.

BACKGROUND: A gene family comprises a group of genes with similar functional domains that play various roles in plant growth, development, and responses to environmental stimuli. Barley (Hordeum vulgare L.) is the fourth most cultivated cereal crop worldwide, and it is an important model species for genetic studies. Systematic identification and annotation of gene families are key for studies of molecular function and evolutionary history. RESULTS: We constructed a multi-omics database containing 5593 genes of 77 gene families called the Barley Gene Family Database (BGFD: http://barleygfdb.com ). BGFD is a free, user-friendly, and web-accessible platform that provides data on barley family genes. BGFD provides intuitive visual displays to facilitate studies of the physicochemical properties, gene structure, phylogenetic relationships, and motif organization of genes. Massive multi-omics datasets have been acquired and processed to generate an atlas of expression pattern profiles and genetic variation in BGFD. The platform offers several practical toolkits to conduct searches, browse, and employ BLAST functions, and the data are downloadable. CONCLUSIONS: BGFD will aid research on the domestication and adaptive evolution of barley; it will also facilitate the screening of candidate genes and exploration of important agronomic traits in barley.

Genomic insights into positive selection during barley domestication.

BACKGROUND: Cultivated barley (Hordeum vulgare) is widely used in animal feed, beverages, and foods and has become a model crop for molecular evolutionary studies. Few studies have examined the evolutionary fates of different types of genes in barley during the domestication process. RESULTS: The rates of nonsynonymous substitution (Ka) to synonymous substitution (Ks) were calculated by comparing orthologous genes in different barley groups (wild vs. landrace and landrace vs. improved cultivar). The rates of evolution, properties, expression patterns, and diversity of positively selected genes (PSGs) and negatively selected genes (NSGs) were compared. PSGs evolved more rapidly, possessed fewer exons, and had lower GC content than NSGs; they were also shorter and had shorter intron, exon, and first exon lengths. Expression levels were lower, the tissue specificity of expression was higher, and codon usage bias was weaker for PSGs than for NSGs. Nucleotide diversity analysis revealed that PSGs have undergone a more severe genetic bottleneck than NSGs. Several candidate PSGs were involved in plant growth and development, which might make them as excellent targets for the molecular breeding of barley. CONCLUSIONS: Our comprehensive analysis of the evolutionary, structural, and functional divergence between PSGs and NSGs in barley provides new insight into the evolutionary trajectory of barley during domestication. Our findings also aid future functional studies of PSGs in barley.

Genome-wide identification, expression analysis and evolutionary relationships of the IQ67-domain gene family in common wheat (Triticum aestivum L.) and its progenitors.

BACKGROUND: The plant-specific IQ67-domain (IQD) gene family plays an important role in plant development and stress responses. However, little is known about the IQD family in common wheat (Triticum aestivum L), an agriculturally important crop that provides more than 20% of the calories and protein consumed in the modern human diet. RESULTS: We identified 125 IQDs in the wheat genome and divided them into four subgroups by phylogenetic analysis. The IQDs belonging to the same subgroup had similar exon-intron structure and conserved motif composition. Polyploidization contributed significantly to the expansion of IQD genes in wheat. Characterization of the expression profile of these genes revealed that a few T. aestivum (Ta)IQDs showed high tissue-specificity. The stress-induced expression pattern also revealed a potential role of TaIQDs in environmental adaptation, as TaIQD-2A-2, TaIQD-3A-9 and TaIQD-1A-7 were significantly induced by cold, drought and heat stresses, and could be candidates for future functional characterization. In addition, IQD genes in the A, B and D subgenomes displayed an asymmetric evolutionary pattern, as evidenced by their different gain or loss of member genes, expression levels and nucleotide diversity. CONCLUSIONS: This study elucidated the potential biological functions and evolutionary relationships of the IQD gene family in wheat and revealed the divergent fates of IQD genes during polyploidization.

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.

Identification of potential oncogenes in triple-negative breast cancer based on bioinformatics analyses.

Triple-negative breast cancer (TNBC) is a subtype with high rates of metastasis, poor prognosis and limited therapeutic options. The present study aimed to identify the potential pivotal genes for prognosis and treatment in TNBC. A total of two microarray expression datasets, GSE38959 and GSE65212, were downloaded from the Gene Expression Omnibus database, and RNA-sequencing data of breast cancer from The Cancer Genome Atlas database were analyzed to screen out differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The intersection of DEGs was submitted to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A protein-protein interaction (PPI) network was constructed and visualized using Cytoscape software. Furthermore, module, centrality and survival analyses were performed to identify the potential hub genes. Reverse transcription-quantitative (RT-q)PCR analysis was performed to detect the expression levels of key genes in TNBC samples, and 377 DEGs were identified. Functional analysis revealed that the DEGs were significantly involved in cell cycle process, nuclear division and the p53 signaling pathway. A PPI network was constructed with these DEGs, and 66 core genes with high centrality features in module 1 were selected. Relapse-free survival analysis confirmed that high expression levels of five genes [cyclin B1 (CCNB1), GINS complex subunit 2, non-SMC condensin I complex subunit G (NCAPG), minichromosome maintenance 4 (MCM4) and ribonucleotide reductase regulatory subunit M2 (RRM2)] were significantly associated with poor prognosis in TNBC. RT-qPCR analysis demonstrated that CCNB1, NCAPG, MCM4 and RRM2 were significantly upregulated in 25 TNBC tissues compared with adjacent normal breast tissues. Furthermore, gene set enrichment analysis revealed that CCNB1, NCAPG, MCM4 and RRM2 were closely associated with tumor proliferation. Taken together, these results suggest that CCNB1, NCAPG, MCM4 and RRM2 are associated with tumorigenesis and TNBC progression, and thus may act as promising prognostic biomarkers and therapeutic targets for TNBC.

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.

Direct Transformation of Glycerol to Propanal using Zirconium Phosphate-Supported Bimetallic Catalysts.

Selective transformation of glycerol to propanal (PA) provides a feasible route towards the sustainable synthesis of high value-added chemicals. In this work, zirconium phosphate (ZrP) was studied as support and Ru and Co as metal sites for glycerol hydrogenolysis in a continuous-flow reactor. It was found that ZrP-supported Co-O species had a moderate selectivity to PA (49.5 %) in glycerol hydrogenolysis. Notably, once Ru species were doped into CoO/ZrP, the resulting catalyst exhibited not only an outstanding catalytic performance for glycerol hydrogenolysis to PA (a selectivity of 80.2 % at full conversion), but also a high stability at least a 50 h long-term performance. The spent catalyst could be regenerated by calcining in air to remove carbonaceous deposits. Characterization indicated that the acid sites on ZrP played a very critical role in the dehydration of glycerol into acrolein (AE), that the distribution of Co was uniform, basically consistent with that of Zr, P and Ru, and that an especially close contact between Co-O and Ru species was formed on Ru/CoO/ZrP catalyst. The further activity tests and characterizations confirmed that there was a strong interaction between the dispersed Co-O species and Ru0 nanoparticles, which endowed Ru sites with high electronic density. This effect could play a role in facilitating the dissociation of H2 , and thus in promoting the hydrogenation reaction. Besides, DFT calculations suggested that the Co-O species can adsorb more strongly the C=C bond of the intermediate AE on a highly coordinatively unsaturated Co (Cocus ) site and thus lead to preferential hydrogenation at the C=C bond of AE to PA.

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.