Biological Interaction Networks Research Articles

Mass spectrometry-based multi-omics analysis reveals distinct molecular features in early and advanced stages of hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omics approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omics results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database for HCC diagnosis, and deduced novel insights and mechanisms related to HCC origination and development, such as glucose depletion during tumor progression, ROCK1 deactivation and GSK3A activation.

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

Exploring the Genetic and Molecular Connection between Autism and Huntington's Disease Via Transcriptomics and Biological Interaction Networks Analysis

The Molecular Landscape of Premature Aging Diseases Defined by Multilayer Network Exploration.

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patientcare. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.

The difference in gene expression network based on gender in the recovery of knee cruciate ligament injuries during exercise

Anterior cruciate ligament (ACL) injuries are common and often debilitating, requiring extensive rehabilitation. The recovery process involves complex physiological, biomechanical, and biochemical factors. Gender differences in the incidence and recovery from ACL injuries are well-documented, with recent advances in molecular biology suggesting that gene expression networks play a crucial role in tissue repair and regeneration. This study aims to compare gene expression networks between genders to provide strategies for better-managing sports injuries. Gene expression data were sourced from the Gene Expression Omnibus (GEO) database, with samples divided into four groups based on gender and age. Differentially expressed genes (DEGs) were identified using GEO2R, and biological interaction networks were constructed using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Network analysis was performed using Gephi, focusing on betweenness centrality and overall centrality. Two common genes, TRNA Aspartic Acid Methyltransferase 1 (TRDMT1) and NCF1, were identified in both sexes but exhibited different centrality measures. NCF1, associated with the production of superoxide anion and linked to chronic granulomatous disease, was expressed ten times higher in men than women. TRDMT1, responsible for RNA methylation, was twice as highly expressed in men. The study underscores the importance of gender-specific molecular mechanisms in ACL injury recovery and highlights the need for personalized treatment strategies.

TARSL: Triple-attention cross-network representation learning to predict synthetic lethality for anti-cancer drug discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exploiting the phenomenon of Synthetic Lethality (SL) by targeting the SL partner of cancer gene. Since traditional methods for SL prediction suffer from high-cost, time-consuming and off-targets effects, computational approaches have been efficient complementary to these methods. Most of existing approaches treat SL associations as independent of other biological interaction networks, and fail to consider other information from various biological networks. Despite some approaches have integrated different networks to capture multi-modal features of genes for SL prediction, these methods implicitly assume that all sources and levels of information contribute equally to the SL associations. As such, a comprehensive and flexible framework for learning gene cross-network representations for SL prediction is still lacking. In this work, we present a novel Triple-Attention cross-network Representation learning for SL prediction (TARSL) by capturing molecular features from heterogeneous sources. We employ three-level attention modules to consider the different contribution of multi-level information. In particular, feature-level attention can capture the correlations between molecular feature and network link, node-level attention can differentiate the importance of various neighbors, and network-level attention can concentrate on important network and reduce the effects of irrelated networks. We perform comprehensive experiments on human SL datasets and these results have proven that our model is consistently superior to baseline methods and predicted SL associations could aid in designing anti-cancer drugs.

Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network.

Although our understanding of how life emerged on Earth from simple organic precursors is speculative, early precursors likely included amino acids. The polymerization of amino acids into peptides and interactions between peptides are of interest because peptides and proteins participate in complex interaction networks in extant biology. However, peptide reaction networks can be challenging to study because of the potential for multiple species and systems-level interactions between species. We developed and employed a computational network model to describe reactions between amino acids to form di-, tri-, and tetra-peptides. Our experiments were initiated with two of the simplest amino acids, glycine and alanine, mediated by trimetaphosphate-activation and drying to promote peptide bond formation. The parameter estimates for bond formation and hydrolysis reactions in the system were found to be poorly constrained due to a network property known as sloppiness. In a sloppy model, the behavior mostly depends on only a subset of parameter combinations, but there is no straightforward way to determine which parameters should be included or excluded. Despite our inability to determine the exact values of specific kinetic parameters, we could make reasonably accurate predictions of model behavior. In short, our modeling has highlighted challenges and opportunities toward understanding the behaviors of complex prebiotic chemical experiments.

Inflammation and Brain Structure in Alzheimer's Disease and Other Neurodegenerative Disorders: a Mendelian Randomization Study.

Previous in vitro and post-mortem studies have reported the role of inflammation in neurodegenerative disorders. However, the association between inflammation and brain structure in vivo and the transcriptome-driven functional basis with relevance to neurodegenerative disorders remains elusive. The aim of the present study is to identify the association among inflammation, brain structure, and neurodegenerative disorders at genetic and transcriptomic levels. Genetic variants associated with inflammatory cytokines were selected from the latest and largest genome-wide association studies of European ancestry. Neurodegenerative disorders including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and dementia with Lewy bodies (DLB) and brain structure imaging measures were selected as the outcomes. Two-sample Mendelian randomization analyses were conducted to identify the causal associations. Single-nucleus transcriptome data of the occipitotemporal cortex was further analyzed to identify the differential expressed genes in AD, which were tested for biological processes and protein interaction network. MR analysis indicated that genetically predicted TREM2 and sTREM2 were significantly associated with AD (TREM2: z-score = -9.088, p-value = 1.02 × 10-19; sTREM2: z-score = -7.495, p-value = 6.61 × 10-14). The present study found no evidence to support the causal associations between other inflammatory cytokines and the risks of AD, PD, ALS, or DLB. Genetically predicted TREM2 was significantly associated with the cortical thickness of inferior temporal (z-score = -4.238, p-value = 2.26 × 10-5) and pole temporal (z-score = -4.549, p-value = 5.40 × 10-6). In the occipitotemporal cortex samples, microglia were the main source of TREM2 gene and showed increasing expression of genes associated with inflammation and immunity. The present study has leveraged genetic and transcriptomic data to identify the association among TREM2, temporal lobe, and AD and the underlying cellular and molecular basis, thus providing a new perspective on the role of TREM2 in AD and insights into the complex associations among inflammation, brain structure, and neurodegenerative disorders, particularly AD.

Genome-wide identification and expression analysis of the AUX/IAA gene family in turnip (Brassica rapa ssp. rapa)

BackgroundAuxin/indoleacetic acid (AUX/IAA) genes encoding short-lived proteins participate in AUX signaling transduction and play crucial roles in plant growth and development. Although the AUX/IAA gene family has been identified in many plants, a systematic analysis of AUX/IAA genes in Brassica rapa ssp. rapa has not yet been reported.ResultsWe performed a comprehensive genome-wide analysis and found 89 AUX/IAA genes in turnip based on the conserved AUX/IAA domain (pfam02309). Phylogenetic analysis of AUX/IAA genes from turnip, Arabidopsis, and cabbage revealed that these genes cluster into six subgroups (A1, A2, A3, A4, B1, and B2). The motif distribution was also conservative among the internal members of the clade. Enhanced yellow fluorescent protein (EYFP) signals of BrrIAA-EYFPs showed that BrrIAA members functioned as nucleoproteins. Moreover, transcriptional analysis revealed that the expression patterns of AUX/IAA genes in turnip were tissue-dependent. Because orthologs have similar biological functions and interaction networks in plant growth and development, BrrIAA66 in turnip possibly played a role in embryo axis formation, vascular development, lateral root formation, and floral organ development by interacting with BrrARF19 and BrrTIR1.ConclusionThese results provide a theoretical basis for further investigation of BrrAUX/IAA genes and lay the foundation for functional analysis of BrrIAA66 in turnip.

A network embedding approach to identify active modules in biological interaction networks.

The identification of condition-specific gene sets from transcriptomic experiments is important to reveal regulatory and signaling mechanisms associated with a given cellular response. Statistical methods of differential expression analysis, designed to assess individual gene variations, have trouble highlighting modules of small varying genes whose interaction is essential to characterize phenotypic changes. To identify these highly informative gene modules, several methods have been proposed in recent years, but they have many limitations that make them of little use to biologists. Here, we propose an efficient method for identifying these active modules that operates on a data embedding combining gene expressions and interaction data. Applications carried out on real datasets show that our method can identify new groups of genes of high interest corresponding to functions not revealed by traditional approaches. Software is available at https://github.com/claudepasquier/amine.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Effect of semaglutide and empagliflozin on cognitive function and hippocampal phosphoproteomic in obese mice.

Objective: Based on the 4D label-free phosphoproteomic technique, we examined the differences in cognitive function and hippocampal phosphorylated protein expression in high-fat diet-induced obese mice after the intervention of semaglutide and empagliflozin, as well as the effects of both on protein activity and function in obese mice's hippocampal tissues and the signaling pathways involved. Methods: Thirty-two C57BL/6JC male mice were assigned to two groups randomly: A control group (group C, 10% of energy is from fat, n = 8) and a high-fat diet group (group H, 60% of energy is from fat, n = 24). The high-fat diet-induced obese mice were screened after 12weeks of feeding based on the criterion that the bodyweight of mice in fat rich diet group was greater than or equal to 20% of the average body weight of the mice in the blank control group. Group H separate into group H (n = 8), group Semaglutide (group S, n = 8), and group empagliflozin (group E, n = 8). For a total of 12weeks, group S received 30nmol/kg/d bodyweight of semaglutide intraperitoneally, group E received 10mg/kg/d bodyweight of empagliflozin via gavage, and groups C and H received equal amounts of saline by intraperitoneal injection and gavage. At the end of treatment, the mice were appraised for cognitive function employing the Morris water maze (MWM), and serum fasting glucose, lipids, and inflammatory parameters were measured. The 4D label-free phosphoproteomics method was employed to screen the differential phosphoproteins and loci in hippocampal tissues of mice in different treatment groups, and bioinformatics was used to analyze the biological processes, signaling pathways, and related protein-protein interaction (PPI) network analysis of these differentially phosphorylated proteins. Results: In comparison to normal controls, The escape latency of obese mice induced by high-fat diet was prolonged, the percentage of swimming time in the target quadrant was reduced, and the number of times of crossing the platform was reduced, whereas semaglutide and empagliflozin treatment reduced escape latency, increase the percentage of swim time in the target quadrant and increase the frequency of passing through the platform area, although there is little difference in the effect of the two drugs. The phosphoproteomic results showed 20,493 unique phosphorylated peptides, representing 21,239 phosphorylation sites and 4,290 phosphorylated proteins. Further analysis revealed that the proteins corresponding to these differentially phosphorylated sites are jointly distributed in signaling pathways such as dopaminergic synapses and axon guidance, and are involved in biological processes such as neuronal projection development, synaptic plasticity, and axonogenesis. Notably, the key factors voltage-dependent L-type calcium channel subunit alpha-1D (CACNA1D), voltage-dependent P/Q-type calcium channel subunit alpha-1A (CACNA1A), and voltage-dependent N-type calcium channel subunit alpha-1B (CACNA1B) were all found to be involved in the dopaminergic synapse pathway, and their expression was upregulated by semaglutide and empagliflozin. Conclusion: We found for the first time that a high-fat diet decreased CACNA1D, CACNA1A, and CACNA1B protein serine phosphorylation, which may affect neuronal development, synaptic plasticity, and cognitive function in mice. Notably, semaglutide and empagliflozin increased the phosphorylation of these proteins.

Graphical characterizations of robust stability in biological interaction networks

Previous studies have inferred robust stability of reaction networks by utilizing linear programs or iterative algorithms. Such algorithms become tedious or computationally infeasible for large networks. In addition, they operate like black boxes without offering intuition for the structures that are necessary to maintain stability. In this work, we provide several graphical criteria for constructing robust stability certificates, checking robust non-degeneracy, verifying persistence, and establishing global stability. By characterizing a set of stability-preserving graph modifications that includes the enzymatic modification motif, we show that the stability of arbitrarily large nonlinear networks can be examined by simple visual inspection. We show applications of this technique to ubiquitous motifs in systems biology such as post-translational modification (PTM) cycles, the ribosome flow model (RFM), T-cell kinetic proofreading, and others. The results of this paper are dedicated in honor of Eduardo D. Sontag’s seventieth birthday and his pioneering work in nonlinear dynamical systems and mathematical systems biology.

Analysis of the Genetic Relationship between Atherosclerosis and Non-Alcoholic Fatty Liver Disease through Biological Interaction Networks.

Non-alcoholic fatty liver disease (NAFLD) seems to have some molecular links with atherosclerosis (ATH); however, the molecular pathways which connect both pathologies remain unexplored to date. The identification of common factors is of great interest to explore some therapeutic strategies to improve the outcomes for those affected patients. Differentially expressed genes (DEGs) for NAFLD and ATH were extracted from the GSE89632 and GSE100927 datasets, and common up- and downregulated DEGs were identified. Subsequently, a protein-protein interaction (PPI) network based on the common DEGs was performed. Functional modules were identified, and the hub genes were extracted. Then, a Gene Ontology (GO) and pathway analysis of common DEGs was performed. DEGs analysis in NAFLD and ATH showed 21 genes that were regulated similarly in both pathologies. The common DEGs with high centrality scores were ADAMTS1 and CEBPA which appeared to be down- and up-regulated in both disorders, respectively. For the analysis of functional modules, two modules were identified. The first one was oriented to post-translational protein modification, where ADAMTS1 and ADAMTS4 were identified, and the second one mainly related to the immune response, where CSF3 was identified. These factors could be key proteins with an important role in the NAFLD/ATH axis.

TAP1, a potential immune-related prognosis biomarker with functional significance in uveal melanoma

BackgroundTAP1 is an immunomodulation-related protein that plays different roles in various malignancies. This study investigated the transcriptional expression profile of TAP1 in uveal melanoma (UVM), revealed its potential biological interaction network, and determined its prognostic value.MethodsCIBERSORT and ESTIMATE bioinformatic methods were used on data sourced from The Cancer Genome Atlas database (TCGA) to determine the correlation between TAP1 expression, UVM prognosis, biological characteristics, and immune infiltration. Gene set enrichment analysis (GSEA) was used to discover the signaling pathways associated with TAP1, while STRING database and CytoHubba were used to construct protein–protein interaction (PPI) and competing endogenous RNA (ceRNA) networks, respectively. An overall survival (OS) prognostic model was constructed to test the predictive efficacy of TAP1, and its effect on the in vitro proliferation activity and metastatic potential of UVM cell line C918 cells was verified by RNA interference.ResultsThere was a clear association between TAP1 expression and UVM patient prognosis. Upregulated TAP1 was strongly associated with a shorter survival time, higher likelihood of metastasis, and higher mortality outcomes. According to GSEA analysis, various immunity-related signaling pathways such as primary immunodeficiency were enriched in the presence of elevated TAP1 expression. A PPI network and a ceRNA network were constructed to show the interactions among mRNAs, miRNAs, and lncRNAs. Furthermore, TAP1 expression showed a significant positive correlation with immunoscore, stromal score, CD8+ T cells, and dendritic cells, whereas the correlation with B cells and neutrophils was negative. The Cox regression model and calibration plots confirmed a strong agreement between the estimated OS and actual observed patient values. In vitro silencing of TAP1 expression in C918 cells significantly inhibited cell proliferation and metastasis.ConclusionsThis study is the first to demonstrate that TAP1 expression is positively correlated with clinicopathological factors and poor prognosis in UVM. In vitro experiments also verified that TAP1 is associated with C918 cell proliferation, apoptosis, and metastasis. These results suggest that TAP1 may function as an oncogene, prognostic marker, and importantly, as a novel therapeutic target in patients with UVM.

Environmental DNA as an emerging tool in botanical research.

Over the past quarter century, environmental DNA (eDNA) has been ascendant as a tool to detect, measure, and monitor biodiversity (species and communities), as a means of elucidating biological interaction networks, and as a window into understanding past patterns of biodiversity. However, only recently has the potential of eDNA been realized in the botanical world. Here we synthesize the state of eDNA applications in botanical systems with emphases on aquatic, ancient, contemporary sediment, and airborne systems, and focusing on both single-species approaches and multispecies community metabarcoding. Further, we describe how abiotic and biotic factors, taxonomic resolution, primer choice, spatiotemporal scales, and relative abundance influence the utilization and interpretation of airborne eDNA results. Lastly, we explore several areas and opportunities for further development of eDNA tools for plants, advancing our knowledge and understanding of the efficacy, utility, and cost-effectiveness, and ultimately facilitating increased adoption of eDNA analyses in botanical systems.

RNA-seq Analysis of the BCG Vaccine in a Humanized Mouse Model

Objective: This study was aimed at screening differentially expressed genes (DEGs) and exploring the potential immune mechanism induced by the Bacillus Calmette-Guerin (BCG) vaccine in a humanized mouse model. Methods: Candidate DEGs between mice vaccinated with BCG or injected with PBS were identified through transcriptomics, and their biological functions, signaling pathways, and protein interaction networks were analyzed through bioinformatics. Results: A total of 1035 DEGs were identified by transcriptomics: 398 up-regulated and 637 down-regulated. GO analysis indicated that these DEGs were significantly enriched in cell adhesion, oxygen transport, receptor complex, carbohydrate binding, serine-type endopeptidase activity, and peroxidase activity terms. KEGG analysis indicated that these DEGs were involved in the Rap1 signaling pathway, axon guidance, PI3K-Akt signaling pathway, natural killer cell mediated cytotoxicity, and cytokine-cytokine receptor interaction. Protein interaction network analysis demonstrated that the Myc, Vegfa, and Itgb3 proteins had the highest aggregation degree, aggregation coefficient, and connectivity. Conclusion: The BCG vaccine induced 1035 DEGs in humanized mice. Among them, the differentially expressed down-regulated genes myc and itgb3 involved in the PI3K-Akt signaling pathway may play essential roles in the immune mechanism of the BCG vaccine.

A Graph Neural Network Approach for the Analysis of siRNA-Target Biological Networks.

Many biological systems are characterised by biological entities, as well as their relationships. These interaction networks can be modelled as graphs, with nodes representing bio-entities, such as molecules, and edges representing relations among them, such as interactions. Due to the current availability of a huge amount of biological data, it is very important to consider in silico analysis methods based on, for example, machine learning, that could take advantage of the inner graph structure of the data in order to improve the quality of the results. In this scenario, graph neural networks (GNNs) are recent computational approaches that directly deal with graph-structured data. In this paper, we present a GNN network for the analysis of siRNA-mRNA interaction networks. siRNAs, in fact, are small RNA molecules that are able to bind to target genes and silence them. These events make siRNAs key molecules as RNA interference agents in many biological interaction networks related to severe diseases such as cancer. In particular, our GNN approach allows for the prediction of the siRNA efficacy, which measures the siRNA's ability to bind and silence a gene target. Tested on benchmark datasets, our proposed method overcomes other machine learning algorithms, including the state-of-the-art predictor based on the convolutional neural network, reaching a Pearson correlation coefficient of approximately 73.6%. Finally, we proposed a case study where the efficacy of a set of siRNAs is predicted for a gene of interest. To the best of our knowledge, GNNs were used for the first time in this scenario.

Expression and Significance of Cyclin-Dependent Protein Kinase 6 in Diffuse Large B-Cell Lymphoma.

ObjectiveTo study the relationship between cyclin-dependent protein kinase 6 (CDK6) expression in diffuse large B-cell lymphoma (DLBCL) and the clinical biological behavior and prognosis.MethodsData mining was performed using the Oncomine and The Cancer Genome Atlas (TCGA) databases to analyze the expression level of CDK6 in DLBCL. CDK6 alterations in DLBCL and related functional networks were analyzed with c-BioPortal and the Gene Set Enrichment Analysis was performed by using DAVID and FunRich software. In addition, screening for differential gene expression of CDK6 was done and enriched by using LinkedOmics. Finally, formalin-fixed and paraffin-embedded (FFPE) tissue samples from 102 patients with DLBCL were collected from the Department of Pathology, Shanxi Cancer Hospital (Taiyuan, Shanxi, China) from January 2015 through December 2020. All cases had complete clinical course records. Thirty cases of lymph node reactive hyperplasia tissues were used as controls. The expression of CDK6 in DLBCL tissues was detected by qRT‑PCR and immunohistochemistry.ResultsBioinformatics analysis: The data showed that mRNA expression level and DNA copy number variations (CNVs) of CDK6 were significantly higher in DLBCL as compared to normal tissue (P ˂ 0.05). Based on C-BioPortal analysis, we speculated that amplification was the most common copy of CDK6 CNV in DLBCL. Through Gene Ontology (GO) analysis of these genes, it was found that the proteins were mainly located in the nucleus and cytoplasm. The biological interaction network of CDK6 alterations were found to participate primarily in the G1-S phase of the process. Analysis of LinkedOmics mRNA sequencing data showed that three genes were positively correlated with CDK6 expression: PSMD1, C2orf29 and ASB1. Through experimental verification, we found that CDK6 was overexpressed in DLBCL, and the expression of CDK6 mRNA and protein in DLBCL were positively correlated with Ann Arbor staging and IPI score (P<0.05), and negatively correlated with overall survival (P<0.001).ConclusionData mining results and experiments revealed and confirmed multi-level evidence for the importance of CDK6 in DLBCL; hence, CDK6 may be a potential marker in DLBCL. Thus, our study will perhaps lay the foundation for further research on the role of CDK6 in the genesis and development of DLBCL.

DENTAL ANOMALIES IN SYNDROMES WITH HYPERTRICHOSIS

Objectives To evaluate dental anomalies in genetic syndromes associated with hypertrichosis and analysis of functional enrichment of protein-protein interaction networks. Study Design A search was performed in the Online Mendelian Inheritance in Man (https://www.omim.org/) electronic database with the associations of the terms “hypertrichosis,” “hirsutism,” and “tooth.” An additional search was performed in PubMed and Orphanet. STRING, analysis of functional enrichment of protein-protein interaction networks, was used to investigate biological processes, pathways, and interaction networks. The P values ​​were subjected to the false discovery rate for correction of several tests, and values ​​≤.05 were considered significant. Results Dental agenesis was the most frequent dental anomaly, present in 40% of the syndromes (n = 16). Forty genes were found, and they participate in 139 biological processes and 3 pathways. The most significant biological processes were disassembly of the nucleosome (P = 1.25e-06), chromosomal organization (P = 1.59e-06), and remodeling of the chromatin (P = 9.52e-06), and the pathways were hepatocellular carcinoma (P = 6.77e-05), thermogenesis (P = .00022), and cell cycle (P = .04). Conclusions The study highlights the protein-protein interaction networks and the most common dental abnormalities in genetic syndromes with hypertrichosis. To evaluate dental anomalies in genetic syndromes associated with hypertrichosis and analysis of functional enrichment of protein-protein interaction networks. A search was performed in the Online Mendelian Inheritance in Man (https://www.omim.org/) electronic database with the associations of the terms “hypertrichosis,” “hirsutism,” and “tooth.” An additional search was performed in PubMed and Orphanet. STRING, analysis of functional enrichment of protein-protein interaction networks, was used to investigate biological processes, pathways, and interaction networks. The P values ​​were subjected to the false discovery rate for correction of several tests, and values ​​≤.05 were considered significant. Dental agenesis was the most frequent dental anomaly, present in 40% of the syndromes (n = 16). Forty genes were found, and they participate in 139 biological processes and 3 pathways. The most significant biological processes were disassembly of the nucleosome (P = 1.25e-06), chromosomal organization (P = 1.59e-06), and remodeling of the chromatin (P = 9.52e-06), and the pathways were hepatocellular carcinoma (P = 6.77e-05), thermogenesis (P = .00022), and cell cycle (P = .04). The study highlights the protein-protein interaction networks and the most common dental abnormalities in genetic syndromes with hypertrichosis.

A Comprehensive Evaluation of Prognostic Value and Immune Infiltration of KDM1 Family in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies in the world. Previous studies indicated that the expression of the KDM1 genes (KDM1s), members of the amine oxidase superfamily, has prognostic value for breast and prostate cancer and malignant neuroblastoma. This study aimed to investigate the expression of KDM1s, their prognostic value, and their correlation with immune infiltration in patients with HCC. Multiomics analyses were utilized to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of KDM1s in patients with HCC. The high expression of KDM1A indicated poor overall survival (OS) and disease-free survival, whereas the high expression of KDM1B was significantly associated with poor OS. The genetic alterations and biological interaction network of KDM1s may provide detailed information for the dysregulated function of KDM1s in patients with HCC. KDM1-related signaling pathways and miRNA targets were explored and may provide value as therapeutic targets or tumor progression markers. The increased mRNA expression of KDM1s was significantly correlated with the infiltration of diverse immune cells in HCC. This data-driven study indicates that KDM1s are promising prognostic biomarkers for survival and have the potential to become novel molecular targets in HCC treatments.