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The mislocalization of proteins leads to breast cancer, one of the world's most prevalent cancers, which can be identified from immunohistochemical images. Here, based on the deep learning framework, location prediction models were constructed using the features of breast immunohistochemical images. Ultimately, six differentially localized proteins that with stable differentially predictive localization, maximum localization differences, and whose predicted results are not affected by removing a single image are obtained (CCNT1, NSUN5, PRPF4, RECQL4, UTP6, ZNF500). Further verification reveals that these proteins are not differentially expressed, but are closely associated with breast cancer and have great classification performance. Potential mechanism analysis shows that their co-expressed or co-located proteins and RNAs may affect their localization, leading to changes in interactions and functions that further causes breast cancer. They have the potential to help shed light on the molecular mechanisms of breast cancer and provide assistance for its early diagnosis and treatment.
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Neoplasias da Mama , Aprendizado Profundo , Imuno-Histoquímica , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Neoplasias da Mama/diagnóstico , Humanos , Feminino , Biomarcadores Tumorais/metabolismo , Biomarcadores Tumorais/genéticaRESUMO
BACKGROUND: The impact of acrylamide (ACR) on learning and memory has garnered considerable attention. However, the targets and mechanisms are still unclear. RESULTS: Elongation factor 2 (eEF2) was significantly upregulated in the results of serum proteomics. Results from in vitro and in vivo experiments indicated a notable upregulation of Eukaryotic elongation factor 2 kinase (eEF2K), the sole kinase responsible for eEF2 phosphorylation, following exposure to ACR (P < 0.05). Subsequent in vitro experiments using eEF2K siRNA and in vivo experiments with eEF2K-knockout mice demonstrated significant improvements in abnormal indicators related to ACR-induced learning and memory deficits (P < 0.05). Proteomic analysis of the hippocampus revealed Lpcat1 as a crucial downstream protein regulated by eEF2K. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that eEF2K may play a role in the process of ACR-induced learning and memory impairment by affecting ether lipid metabolism. CONCLUSIONS: In summary, eEF2K as a pivotal treatment target in the mechanisms underlying ACR-induced learning and memory impairment, and studies have shown that it provides robust evidence for potential clinical interventions targeting ACR-induced impairments.
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Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
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Algoritmos , Reposicionamento de Medicamentos , Reposicionamento de Medicamentos/métodos , Humanos , Máquina de Vetores de Suporte , Biologia Computacional/métodos , Neoplasias/tratamento farmacológico , Doenças Cardiovasculares/tratamento farmacológicoRESUMO
Background: Tillage practices can substantially affect soil properties depending on crop stage. The interaction between tillage and crop growth on arbuscular mycorrhizal fungi (AMF) communities remains unclear. We investigated the interactions between four tillage treatments (CT: conventional tillage, RT: reduced tillage, NT: no tillage with mulch, and SS: subsoiling with mulch), maintained for 25 years, and two wheat growth stages (elongation stage and grain filling stage) on AMF diversity and community composition. Results: The AMF community composition strongly changed during wheat growth, mainly because of changes in the relative abundance of dominant genera Claroideoglomus, Funneliformi, Rhizophagu, Entrophospora, and Glomus. Co-occurrence network analysis revealed that the grain filling stage had a more complex network than the elongation stage. Redundancy analysis results showed that keystone genera respond mainly to changes in soil organic carbon during elongation stage, whereas the total nitrogen content affected the keystone genera during grain filling. Compared with CT, the treatments with mulch, i.e., NT and SS, significantly changed the AMF community composition. The change of AMF communities under different tillage practices depended on wheat biomass and soil nutrients. NT significantly increased the relative abundances of Glomus and Septoglomus, while RT significantly increased the relative abundance of Claroideoglomus. Conclusion: Our findings indicate that the relative abundance of dominant genera changed during wheat growth stages. Proper tillage practices (e.g., NT and SS) benefit the long-term sustainable development of the Loess Plateau cropping systems.
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Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.
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Adenocarcinoma de Pulmão , Aprendizado Profundo , Neoplasias Pulmonares , Humanos , Prognóstico , Adenocarcinoma de Pulmão/genética , Biomarcadores Tumorais/genética , Neoplasias Pulmonares/genéticaRESUMO
OBJECTIVE: The objective of the study was to systemically evaluate the clinical efficacy of minimally invasive stereotactic puncture for intracranial hematoma evacuation in patients with cerebral hemorrhage. MATERIALS AND METHODS: Relevant studies in PubMed, Web of Science, MEDLINE, China National Knowledge Infrastructure, Wanfang, and VIP databases were searched. A meta-analysis was performed following the inclusion and exclusion criteria screening, data extraction, and literature quality evaluation. RESULTS: Fifteen studies involving 1312 patients were included with 673 participants in the experimental group and 639 in the control group. The results of the meta-analysis showed that, compared with traditional craniotomy or treatment, minimally invasive stereotactic puncture intracranial hematoma removal had a higher clinical total effective rate in patients with cerebral hemorrhage, an outcome that could significantly shorten the hospitalization time of patients with cerebral hemorrhage. The level of post-operative activities of daily living was significantly higher, the incidence of postoperative complications was lower, and the mortality rate was lower. However, there was no significant difference in the degree of post-operative neurological deficit. CONCLUSION: Compared with traditional craniotomy or conservative treatment, minimally invasive stereotactic puncture intracranial hematoma removal has a higher clinical efficacy in the treatment of patients with cerebral hemorrhage, which can improve the post-operative daily life and abilities of patients.
OBJETIVO: Evaluación sistemática de la eficacia clínica de la punción estereotáctica mínimamente invasiva para la evacuación de hematomas intracraneales en pacientes con hemorragia cerebral. MATERIAL Y MÉTODOS: Se realizaron búsquedas en estudios relevantes en PubMed, Web of Science, MEDLINE, Infraestructura Nacional de Conocimiento de China, base de datos Wanfang y base de datos VIP. El metanálisis se realizó después de la selección de criterios de inclusión y exclusión, la extracción de datos y la evaluación de la calidad de la literatura. RESULTADOS: Se incluyeron 15 estudios en los que participaron 1.312 sujetos, 673 en el grupo experimental y 639 en el grupo control. En comparación con la Craneotomía tradicional o el tratamiento, el aclaramiento estereotáctico mínimamente invasivo de hematomas intracraneales tiene una alta eficiencia clínica total en pacientes con hemorragia intracerebral y puede acortar significativamente el tiempo de hospitalización de los pacientes con hemorragia intracerebral. El nivel de actividad de la vida diaria postoperatoria (ADL) aumentó significativamente, la incidencia de complicaciones postoperatorias disminuyó y la mortalidad disminuyó. Sin embargo, no hubo diferencia significativa en el grado de déficit neurológico postoperatorio. CONCLUSIÓN: En comparación con la Craneotomía tradicional o el tratamiento conservador, la Craneotomía estereotáctica mínimamente invasiva tiene un mayor efecto clínico en el tratamiento de la hemorragia cerebral y puede mejorar la capacidad de la vida diaria de los pacientes después de la operación.
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Atividades Cotidianas , Hemorragia Cerebral , Humanos , Hemorragia Cerebral/etiologia , Hemorragia Cerebral/cirurgia , Punções , Craniotomia/métodos , Resultado do Tratamento , Hematoma/etiologia , Hematoma/cirurgiaRESUMO
Introduction: Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy. Methods: This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites. Results: Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models. Conclusion: To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Humanos , Proteômica , Adenocarcinoma de Pulmão/genética , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Biomarcadores , RNA Mensageiro/metabolismoRESUMO
BACKGROUND: Cell-cell communication by carcinoma-derived exosomes can influence the tumor microenvironment (TME) and regulate cancer progression. Based on the overexpression of microRNA-21-5p (miR-21) in plasma from patients diagnosed with esophageal squamous cell carcinoma (ESCC) and exosomes from ESCC cell lines identified earlier, this study aimed to explore the influence of exosomal miR-21 within the TME. METHOD: ScRNA-Seq and Bulk RNA-Seq were integrated to elucidate the communication between cancer and endothelial cells. The functionality and mechanisms by which exo-miR-21 derived from carcinoma regulate endothelial cell-mediated angiogenesis were assessed using a cocultivation model of EC9706 cells and recipient human umbilical vein endothelial cells (HUVECs), through blood vessel formation experiments, luciferase reporter assays, RT-qPCR, and western blot analysis. RESULT: A total of 3842 endothelial cells were extracted from the scRNA-seq data of ESCC samples and reclustered into five cell subtype. Cell-cell communication analysis revealed cancer cells presented a strong interaction with angiogenesis-like endothelial cells in secreted signaling. MiR-21 was unregulated in ESCC and the carcinoma-derived exo-miR-21 was significantly raised in HUVECs. The exo-miR-21 promoted the proliferation and migration of HUVECs while also enhancing, closed mesh count, and junction number in HUVECs. Mechanistically, dual-luciferase reporter assay revealed that PTEN was the target of miR-21. Meanwhile, p-Akt was significantly increased and suppressed by inhibition of miR-21 and PI3K inhibitor LY294002. CONCLUSION: Exo-miR-21-mediated communication between endothelial and cancer cells plays a pivotal role in promoting the angiogenesis of ESCC. Therefore, controlling exo-miR-21 could serve as a novel therapeutic strategy for ESCC by targeting angiogenesis.
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Neoplasias Esofágicas , Carcinoma de Células Escamosas do Esôfago , MicroRNAs , Humanos , Neoplasias Esofágicas/patologia , Carcinoma de Células Escamosas do Esôfago/patologia , MicroRNAs/genética , MicroRNAs/metabolismo , Células Endoteliais da Veia Umbilical Humana/metabolismo , Células Endoteliais da Veia Umbilical Humana/patologia , Fosfatidilinositol 3-Quinases/metabolismo , Microambiente Tumoral , Neovascularização Patológica/genética , Neovascularização Patológica/metabolismo , Comunicação , Luciferases/metabolismo , Proliferação de Células , Linhagem Celular TumoralRESUMO
Introduction: Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction. Methods: In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established. Results: Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction. Discussion: This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.
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Orthodenticle homeobox (OTX1) is reported to be involved in numerous cancers, but the expression level and molecular function of OTX1 in gallbladder cancer (GBC) remain unknown. Here, we found the elevated level of OTX1 associated with poor prognosis in human gallbladder cancer. In vitro and in vivo studies of human gallbladder cancer cell lines demonstrated that overexpression of OTX1 promoted cell proliferation, whereas the downregulation inhibited it. Additionally, we found a tight correlation between the serum level of taurodeoxycholic acid (TDCA) and OTX1 expression. TDCA-induced activation of YAP1 by phosphorylation inhibition contributed to the transcriptional activation of OTX1. Mechanistically, we identified that OTX1 activated AKT signaling pathway by transactivating the expression of IFITM3 and thus promoted the proliferation of GBC cells. Taken together, our results showed that TDCA-YAP1-dependent expression of OTX1 regulated IFITM3 and affected GBC proliferation via the AKT signaling pathway. Our experiments also suggested that OTX1 is a novel therapeutic target for GBC.
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Neoplasias da Vesícula Biliar , Humanos , Linhagem Celular Tumoral , Proliferação de Células/fisiologia , Neoplasias da Vesícula Biliar/metabolismo , Proteínas de Membrana/metabolismo , Fatores de Transcrição Otx/metabolismo , Proteínas Proto-Oncogênicas c-akt/metabolismo , Proteínas de Ligação a RNA/metabolismo , Transdução de Sinais , Ácido Taurodesoxicólico/farmacologiaRESUMO
Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Antineoplásicos , Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Reposicionamento de Medicamentos , Neoplasias Pulmonares/tratamento farmacológico , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Biologia ComputacionalRESUMO
Background: Esophageal cancer (EC) had the sixth-highest mortality rate of all cancers due to its poor prognosis. Immune cells and mutation genes influenced the prognosis of EC, but their combined effect on predicting EC prognosis was unknown. In this study, we comprehensively analyzed the immune cell infiltration (ICI) and mutation genes and their combined effects for predicting prognosis in EC. Methods: The CIBERSORT and ESTIMATE algorithms were used to analyse the ICI scape based on the TCGA and GEO databases. EC tissues and pathologic sections from Huai'an, China, were used to verify the key immune cells and mutation genes and their interactions. Results: Stromal/immune score patterns and ICI/gene had no statistical significance in overall survival (OS) (p > 0.05). The combination of ICI and tumor mutation burden (TMB) showed that the high TMB and high ICI score group had the shortest OS (p = 0.004). We recognized that the key mutation gene NRF2 was significantly different in the high/low ICI score subgroups (p = 0.002) and positivity with mast cells (MCs) (p < 0.05). Through experimental validation, we found that the MCs and activated mast cells (AC-MCs) were more infiltration in stage II/III (p = 0.032; p = 0.013) of EC patients and that NRF2 expression was upregulated in EC (p = 0.045). AC-MCs combined with NRF2 had a poor prognosis, according to survival analysis (p = 0.056) and interactive analysis (p = 0.032). Conclusions: We presume that NRF2 combined with AC-MCs could be a marker to predict prognosis and could influence immunotherapy through regulating PD-L1 in the EC.
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Skeletal muscle has a robust endocrine function as a powerful organ and can secrete and release cytokines or polypeptides known as myokines. These myokines have significant regulatory effects on signal transduction in skeletal muscle and the metabolism of peripheral tissues and organs and exert biological effects via autocrine, paracrine, or endocrine forms. Obesity and aging cause myokine secretion dysregulation, and hastening sarcopenic obesity (SO) development. Exercise is currently an excellent intervention and prevention method for SO. Meanwhile, exercise impacts many organs and tissues. These organs and tissues will produce various myokines in response to movement and metabolism throughout the body to govern muscle differentiation, growth, and remodeling. According to accumulating data, exercise can increase the release of myokines from diverse tissues into the blood and postpone the SO onset and progression by influencing protein metabolism, inflammation, mitochondrial quality control, and other mechanisms.
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Citocinas , Sarcopenia , Humanos , Citocinas/metabolismo , Sarcopenia/terapia , Sarcopenia/metabolismo , Exercício Físico/fisiologia , Músculo Esquelético/metabolismo , Obesidade/terapia , Obesidade/metabolismoRESUMO
Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein-protein interaction networks. A total of 69, 43, and 64 leader genes were identified for stomach, liver, and colon cancers, respectively. Furthermore, literature reviews and experimental data including protein expression levels and independent datasets from other databases all verified their association with corresponding cancer types. These final leader genes were expected to be used as diagnostic biomarkers and targets for new treatment strategies. The procedure for identifying cancer leader genes could be expanded to open up a window into the mechanisms, early diagnosis, and treatment of other cancer types.
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Lung adenocarcinoma is the most common type of primary lung cancer, but the regulatory mechanisms during carcinogenesis remain unclear. The identification of regulatory modules for lung adenocarcinoma has become one of the hotspots of bioinformatics. In this paper, multiple deep neural network (DNN) models were constructed using the expression data to identify regulatory modules for lung adenocarcinoma in biological networks. First, the mRNAs, lncRNAs and miRNAs with significant differences in the expression levels between tumor and non-tumor tissues were obtained. MRNA DNN models were established and optimized to mine candidate mRNAs that significantly contributed to the DNN models and were in the center of an interaction network. Another DNN model was then constructed and potential ceRNAs were screened out based on the contribution of each RNA to the model. Finally, three modules comprised of miRNAs and their regulated mRNAs and lncRNAs with the same regulation direction were identified as regulatory modules that regulated the initiation of lung adenocarcinoma through ceRNAs relationships. They were validated by literature and functional enrichment analysis. The effectiveness of these regulatory modules was evaluated in an independent lung adenocarcinoma dataset. Regulatory modules for lung adenocarcinoma identified in this study provided a reference for regulatory mechanisms during carcinogenesis.
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BACKGROUND: Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. RESULTS: Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. CONCLUSION: The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.
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Cardiomiopatias , Cardiomiopatia Dilatada , Isquemia Miocárdica , Biomarcadores/metabolismo , Cardiomiopatias/genética , Cardiomiopatia Dilatada/genética , Humanos , Redes e Vias Metabólicas/genéticaRESUMO
Glioma is one particular type of brain malignancy which is highly complex and usually has a poor prognosis. Despite the limited diagnostic level of glioma, the survival time of affected patients broadly varies. Here, we conducted a detailed analysis, regarding the differences in patient survival time, to discover potential survival-related genes in glioma as well as their putative regulatory mechanisms. To contextualize the acquisition of these potential prognosis markers in large populations, particularly in China, we combined CGGA and The Cancer Genome Atlas (TCGA) databases to properly identify genes that are significantly related to survival. Our workflow combined a series of analytical approaches, including differential analysis, survival time, co-expression, clinical correlation analysis, ROC curve evaluation and prediction ability. Our results indicate that the four particular genes - PLAT, IGFBP2, BCAT1, SERPINH1 could be used as independent prognostic marker genes. These genes have also shown good prognostic ability in distinct populations, reiterating the robustness and value of these prognostic markers.
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Neoplasias Encefálicas , Glioma , Neoplasias Encefálicas/genética , China , Análise de Dados , Glioma/genética , Humanos , TransaminasesRESUMO
Ischemic cardiomyopathy (ICM) is a common human heart disease that causes death. No effective biomarkers for ICM could be found in existing databases, which is detrimental to the in-depth study of this disease. In the present study, ICM susceptibility biomarkers were identified using a proposed strategy based on RNA-Seq and miRNA-Seq data of ICM and normal samples. Significantly differentially expressed competing endogenous RNA (ceRNA) triplets were constructed using permutation tests and differentially expressed mRNAs, miRNAs and lncRNAs. Candidate ICM susceptible genes were screened out as differentially expressed genes in significantly differentially expressed ceRNA triplets enriched in ICM-related functional classes. Finally, eight ICM susceptibility genes and their significantly correlated lncRNAs with high classification accuracy were identified as ICM susceptibility biomarkers. These biomarkers would contribute to the diagnosis and treatment of ICM. The proposed strategy could be extended to other complex diseases without disease biomarkers in public databases.
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Biomarcadores/metabolismo , Cardiomiopatias/diagnóstico , Cardiomiopatias/genética , RNA/genética , Redes Reguladoras de Genes/genética , HumanosRESUMO
Breast cancer is the most common female death-causing cancer worldwide. A network-based integration method was proposed to identify potential breast cancer genes. First, genes were prioritized using a gene prioritization algorithm by the strategy of disease risks transferred between genes in a network with weighted vertexes and edges. Our prioritization algorithm was effectives and robust for top-ranked seed gene number and higher area under the curve values compared to ToppGene and ToppNet. Then, 20 potential breast cancer genes were identified as common genes of the top 50 candidate genes for their robustness in multiple prioritizations. These genes could accurately classify tumor and normal samples of all and paired sample sets and three independent datasets. Of potential breast cancer genes, 18 were verified by literature and 2 were novel genes that need further study. This study would contribute to the understanding of the genetic architecture for the diagnosis and treatment of breast cancer.
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Biomarcadores Tumorais/genética , Neoplasias da Mama/genética , Proteínas de Neoplasias/genética , Algoritmos , Área Sob a Curva , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/patologia , Feminino , Regulação Neoplásica da Expressão Gênica/genética , Redes Reguladoras de Genes/genética , Humanos , Curva ROCRESUMO
Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.