Connections Between Endoplasmic Reticulum Stress and Prognosis of Hepatocarcinoma.

Endoplasmic reticulum (ER) stress is a state in which misfolded or unfolded proteins accumulate in the lumen of the ER as a result of some exogenous or endogenous factors. It plays a crucial role in the pathogenesis of malignancies, affecting cell survival, proliferation, and metastasis in cancer. ER stress genes could provide new ideas for potential therapeutic targets in cancer.

IDO1-mediated kynurenine production inhibits IGFBP5 signaling to promote 5-fluorouracil-induced senescence escape and chemoresistance in colorectal cancer.

Cellular senescence is an irreversible state of growth arrest, and induction of senescence is considered a potential therapeutic strategy against cancer. Indoleamine 2,3-dioxygenase 1 (IDO1), an enzyme catabolizing L-tryptophan into kynurenine, plays a key role in tumor immune tolerance. However, the roles of IDO1 in cellular senescence and chemoresistance remain elusive.

Differentially localized protein identification for breast cancer based on deep learning in immunohistochemical images.

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).

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm.

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.

Plant and soil responses to tillage practices change arbuscular mycorrhizal fungi populations during crop growth.

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.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.

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.

The clinical effect of minimally invasive stereotactic puncture intracranial hematoma removal in the treatment of patients with cerebral hemorrhage: a meta-analysis.

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.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma.

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.

Long-term conservation tillage improves soil stoichiometry balance and crop productivity based on a 17-year experiment in a semi-arid area of northern China.

Although conservation tillage has been widely implemented to address the challenge to improve crop yield and soil quality with fewer environmental costs, its long-term effects on crop yields and soil stoichiometry balance remain uncertain. Here, four different long-term (17-year) tillage practices (conventional tillage (CT), deep scarification (DS), no tillage (NT), and ridge tillage (RT)) were conducted in northern China to evaluate their effects on crop yield, soil nutrients, C sequestration, and soil stoichiometry. The conservation tillage (DS, NT, and RT) increased the recent 5-year average yields by 12.

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis.

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.

Taurodeoxycholic acid-YAP1 upregulates OTX1 in promoting gallbladder cancer malignancy through IFITM3-dependent AKT activation.

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.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.

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.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer.

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.

Myokine, a key cytokine for physical exercise to alleviate sarcopenic obesity.

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.

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks.

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.

Integrating Expression Data-Based Deep Neural Network Models with Biological Networks to Identify Regulatory Modules for Lung Adenocarcinoma.

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.

Identification of cardiomyopathy-related core genes through human metabolic networks and expression data.

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.

Identification of genes associated with cancer prognosis in glioma: data analyses of Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA).

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.

Pigment Epithelium-Derived Factor Promotes the Growth and Migration of Human Esophageal Squamous Cell Carcinoma.

Pigment epithelium-derived factor (PEDF) is an oncogene found in various types of cancers. However, how PEDF affects the development of human esophageal squamous cell carcinoma (ESCC) is unknown. This study investigates the role of PEDF in ESCC cell proliferation, migration, and cell cycle both and .

Network-based integration method for potential breast cancer gene identification.

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.

A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy.

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.

Identification of breast cancer risk modules via an integrated strategy.

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.