PPIH acts as a potential predictive biomarker for patients with common solid tumors.

BACKGROUND: Our previous studies have indicated that mRNA and protein levels of PPIH are significantly upregulated in Hepatocellular Carcinoma (LIHC) and could act as predictive biomarkers for patients with LIHC. Nonetheless, the expression and implications of PPIH in the etiology and progression of common solid tumors have yet to be explored, including its potential as a serum tumor marker. METHODS: We employed bioinformatics analyses, augmented with clinical sample evaluations, to investigate the mRNA and protein expression and gene regulation networks of PPIH in various solid tumors. We also assessed the association between PPIH expression and overall survival (OS) in cancer patients using Kaplan-Meier analysis with TCGA database information. Furthermore, we evaluated the feasibility and diagnostic efficacy of PPIH as a serum marker by integrating serological studies with established clinical tumor markers. RESULTS: Through pan-cancer analysis, we found that the expression levels of PPIH mRNA in multiple tumors were significantly different from those in normal tissues. This study is the first to report that PPIH mRNA and protein levels are markedly elevated in LIHC, Colon adenocarcinoma (COAD), and Breast cancer (BC), and are associated with a worse prognosis in these cancer patients. Conversely, serum PPIH levels are decreased in patients with these tumors (LIHC, COAD, BC, gastric cancer), and when combined with traditional tumor markers, offer enhanced sensitivity and specificity for diagnosis. CONCLUSION: Our findings propose that PPIH may serve as a valuable predictive biomarker in tumor patients, and its secreted protein could be a potential serum marker, providing insights into the role of PPIH in cancer development and progression.

Differential expression of circRNAs during osteogenic/odontogenic differentiation of stem cells from apical papilla promoted by blue light-emitting diode.

BACKGROUND: Circular RNA (circRNA) is a key player in regulating the multidirectional differentiation of stem cells. Previous research by our group found that the blue light-emitting diode (LED) had a promoting effect on the osteogenic/odontogenic differentiation of human stem cells from apical papilla (SCAPs). This research aimed to investigate the differential expression of circRNAs during the osteogenic/odontogenic differentiation of SCAPs regulated by blue LED. MATERIALS AND METHODS: SCAPs were divided into the irradiation group (4 J/cm2) and the control group (0 J/cm2), and cultivated in an osteogenic/odontogenic environment. The differentially expressed circRNAs during osteogenic/odontogenic differentiation of SCAPs promoted by blue LED were detected by high-throughput sequencing, and preliminarily verified by qRT-PCR. Functional prediction of these circRNAs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the circRNA-miRNA-mRNA networks were also constructed. RESULTS: It showed 301 circRNAs were differentially expressed. GO and KEGG analyses suggested that these circRNAs were associated with some signaling pathways related to osteogenic/odontogenic differentiation. And the circRNA-miRNA-mRNA networks were also successfully constructed. CONCLUSION: CircRNAs were involved in the osteogenic/odontogenic differentiation of SCAPs promoted by blue LED. In this biological process, circRNA-miRNA-mRNA networks served an important purpose, and circRNAs regulated this process through certain signaling pathways.

Prognostic significance of CHCHD2P9 and ZNF204P in breast cancer: exploring their expression patterns and associations with malignancy-related genes.

BACKGROUND: Non-coding RNAs (ncRNAs) have a crucial impact on diverse cellular processes, influencing the progression of breast cancer (BC). The objective of this study was to identify novel ncRNAs in BC with potential effects on patient survival and disease progression. METHODS: We utilized the cancer genome atlas data to identify ncRNAs associated with BC pathogenesis. We explored the association between these ncRNA expressions and survival rates. A risk model was developed using candidate ncRNA expression and beta coefficients obtained from a multivariate Cox regression analysis. Co-expression networks were constructed to determine potential relationships between these ncRNAs and molecular pathways. For validation, we employed BC samples and the RT-qPCR method. RESULTS: Our findings revealed a noteworthy increase in the expression of AC093850.2 and CHCHD2P9 in BC, which was correlated with a poor prognosis. In contrast, ADAMTS9-AS1 and ZNF204P displayed significant downregulation and were associated with a favorable prognosis. The risk model, incorporating these four ncRNAs, robustly predicted patient survival. The co-expression network showed an effective association between levels of AC093850.2, CHCHD2P9, ADAMTS9-AS1, and ZNF204P and genes involved in pathways like metastasis, angiogenesis, metabolism, and DNA repair. The RT-qPCR results verified notable alterations in the expression of CHCHD2P9 and ZNF204P in BC samples. Pan-cancer analyses revealed alterations in the expression of these two ncRNAs across various cancer types. CONCLUSION: This study presents a groundbreaking discovery, highlighting the substantial dysregulation of CHCHD2P9 and ZNF204P in BC and other cancers, with implications for patient survival.

Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis.

Background: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive. Methods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer. Results: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers. Conclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

Weighted gene co-expression network analysis reveals genes related to growth performance in Hu sheep.

Hu sheep are a unique breed in our country with great reproductive potential, the extent of whose breeding has been steadily rising in recent years. The study subjects in this experiment were 8-month-old Hu sheep (n = 112). First of all, the growth performance, slaughter performance and meat quality of their eye muscle quality were assessed, meanwhile their live weight, carcass weight, body length, body height, chest circumference, chest depth and tube circumference were respectively 33.81 ± 5.47 kg, 17.43 ± 3.21 kg, 60.36 ± 4.41 cm, 63.25 ± 3.88 cm, 72.03 ± 5.02 cm, 30.70 ± 2.32 cm and 7.36 ± 0.56 cm, with a significant difference between rams and ewes (P < 0.01). Following that, transcriptome sequencing was done, and candidate genes related to growth performance were identified using the weighted co-expression network analysis (WGCNA) approach, which was used to identified 15 modules, with the turquoise and blue modules having the strongest association with growth and slaughter performance, respectively. We discovered hub genes such as ARHGAP31, EPS8, AKT3, EPN1, PACS2, KIF1C, C12H1orf115, FSTL1, PTGFRN and IFIH1 in the gene modules connected with growth and slaughter performance. Our research identifies the hub genes associated with the growth and slaughter performance of Hu sheep, which play an important role in their muscle growth, organ and cartilage development, blood vessel development and energy metabolic pathways. Our findings might lead to the development of potentially-useful biomarkers for the selection of growth and slaughterer performance-related attributes of sheep and other livestock.

Time-course swRNA-seq uncovers a hierarchical gene regulatory network in controlling the response-repair-remodeling after wounding.

Wounding initiates intricate responses crucial for tissue repair and regeneration. Yet, the gene regulatory networks governing wound healing remain poorly understood. Here, employing single-worm RNA sequencing (swRNA-seq) across 12 time-points, we delineated a three-stage wound repair process in C. elegans: response, repair, and remodeling. Integrating diverse datasets, we constructed a dynamic regulatory network comprising 241 transcription regulators and their inferred targets. We identified potentially seven autoregulatory TFs and five cross-autoregulatory loops involving pqm-1 and jun-1. We revealed that TFs might interact with chromatin factors and form TF-TF combinatory modules via intrinsically disordered regions to enhance response robustness. We experimentally validated six regulators functioning in transcriptional and translocation-dependent manners. Notably, nhr-76, daf-16, nhr-84, and oef-1 are potentially required for efficient repair, while elt-2 may act as an inhibitor. These findings elucidate transcriptional responses and hierarchical regulatory networks during C. elegans wound repair, shedding light on mechanisms underlying tissue repair and regeneration.

Osteosarcoma in a ceRNET perspective.

Osteosarcoma (OS) is the most prevalent and fatal type of bone tumor. It is characterized by great heterogeneity of genomic aberrations, mutated genes, and cell types contribution, making therapy and patients management particularly challenging. A unifying picture of molecular mechanisms underlying the disease could help to transform those challenges into opportunities.This review deeply explores the occurrence in OS of large-scale RNA regulatory networks, denominated "competing endogenous RNA network" (ceRNET), wherein different RNA biotypes, such as long non-coding RNAs, circular RNAs and mRNAs can functionally interact each other by competitively binding to shared microRNAs. Here, we discuss how the unbalancing of any network component can derail the entire circuit, driving OS onset and progression by impacting on cell proliferation, migration, invasion, tumor growth and metastasis, and even chemotherapeutic resistance, as distilled from many studies. Intriguingly, the aberrant expression of the networks components in OS cells can be triggered also by the surroundings, through cytokines and vesicles, with their bioactive cargo of proteins and non-coding RNAs, highlighting the relevance of tumor microenvironment. A comprehensive picture of RNA regulatory networks underlying OS could pave the way for the development of innovative RNA-targeted and RNA-based therapies and new diagnostic tools, also in the perspective of precision oncology.

Exploring the mechanisms of glycolytic genes involvement in pulmonary arterial hypertension through integrative bioinformatics analysis.

The purpose of this study was to identify the mechanisms underlying the involvement of glycolytic genes in pulmonary arterial hypertension (PAH). This study involved downloading 3 datasets from the GEO database at the National Center for Biotechnology Information. The datasets were processed to obtain expression matrices for analysis. Genes involved in glycolysis-related pathways were obtained, and genes related to glycolysis were selected based on significant differences in expression. Gene Ontology functional annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and GSEA enrichment analysis were performed on the DEGs. Combining LASSO regression with SVM-RFE machine learning technology, a PAH risk prediction model based on glycolysis related gene expression was constructed, and CIBERSORTx technology was used to analyse the immune cell composition of PAH patients. Gene enrichment analysis revealed that the DEGs work synergistically across multiple biological pathways. A total of 6 key glycolysis-related genes were selected using LASSO regression and SVM. A bar plot was constructed to evaluate the weights of the key genes and predict the risk of PAH. The clinical application value and predictive accuracy of the model were assessed. Immunological feature analysis revealed significant correlations between key glycolysis-related genes and the abundances of different immune cell types. The glycolysis genes (ACSS2, ALAS2, ALDH3A1, ADOC3, NT5E, and TALDO1) identified in this study play important roles in the development of pulmonary arterial hypertension, providing new evidence for the involvement of glycolysis in PAH.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature.

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Gene regulatory landscape of cerebral cortex folding.

Folding of the cerebral cortex is a key aspect of mammalian brain development and evolution, and defects are linked to severe neurological disorders. Primary folding occurs in highly stereotyped patterns that are predefined in the cortical germinal zones by a transcriptomic protomap. The gene regulatory landscape governing the emergence of this folding protomap remains unknown. We characterized the spatiotemporal dynamics of gene expression and active epigenetic landscape (H3K27ac) across prospective folds and fissures in ferret. Our results show that the transcriptomic protomap begins to emerge at early embryonic stages, and it involves cell-fate signaling pathways. The H3K27ac landscape reveals developmental cell-fate restriction and engages known developmental regulators, including the transcription factor Cux2. Manipulating Cux2 expression in cortical progenitors changed their proliferation and the folding pattern in ferret, caused by selective transcriptional changes as revealed by single-cell RNA sequencing analyses. Our findings highlight the key relevance of epigenetic mechanisms in defining the patterns of cerebral cortex folding.

Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma.

Cuproptosis is a newly defined form of programmed cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis and metastasis. However, whether cuproptosis-related lncRNAs are involved in the pathogenesis of diffuse large B cell lymphoma (DLBCL) remains unclear. This study aimed to identify the prognostic signatures of cuproptosis-related lncRNAs in DLBCL and investigate their potential molecular functions. RNA-Seq data and clinical information for DLBCL were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Cuproptosis-related lncRNAs were screened out through Pearson correlation analysis. Utilizing univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis, we identified seven cuproptosis-related lncRNAs and developed a risk prediction model to evaluate its prognostic value across multiple groups. GO and KEGG functional analyses, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. Additionally, drug sensitivity analysis identified drugs with potential efficacy in DLBCL. Finally, the protein-protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). We identified a set of seven cuproptosis-related lncRNAs including LINC00294, RNF139-AS1, LINC00654, WWC2-AS2, LINC00661, LINC01165 and LINC01398, based on which we constructed a risk model for DLBCL. The high-risk group was associated with shorter survival time than the low-risk group, and the signature-based risk score demonstrated superior prognostic ability for DLBCL patients compared to traditional clinical features. By analyzing the immune landscapes between two groups, we found that immunosuppressive cell types were significantly increased in high-risk DLBCL group. Moreover, functional enrichment analysis highlighted the association of differentially expressed genes with metabolic, inflammatory and immune-related pathways in DLBCL patients. We also found that the high-risk group showed more sensitivity to vinorelbine and pyrimethamine. A cuproptosis-related lncRNA signature was established to predict the prognosis and provide insights into potential therapeutic strategies for DLBCL patients.

An integrated bioinformatic analysis of microarray datasets to identify biomarkers and miRNA-based regulatory networks in leishmaniasis.

Micro RNAs (miRNAs, miRs) and relevant networks might exert crucial functions during differential host cell infection by the different Leishmania species. Thus, a bioinformatic analysis of microarray datasets was developed to identify pivotal shared biomarkers and miRNA-based regulatory networks for Leishmaniasis. A transcriptomic analysis by employing a comprehensive set of gene expression profiling microarrays was conducted to identify the key genes and miRNAs relevant for Leishmania spp. infections. Accordingly, the gene expression profiles of healthy human controls were compared with those of individuals infected with Leishmania mexicana, L. major, L. donovani, and L. braziliensis. The enrichment analysis for datasets was conducted by utilizing EnrichR database, and Protein-Protein Interaction (PPI) network to identify the hub genes. The prognostic value of hub genes was assessed by using receiver operating characteristic (ROC) curves. Finally, the miRNAs that interact with the hub genes were identified using miRTarBase, miRWalk, TargetScan, and miRNet. Differentially expressed genes were identified between the groups compared in this study. These genes were significantly enriched in inflammatory responses, cytokine-mediated signaling pathways and granulocyte and neutrophil chemotaxis responses. The identification of hub genes of recruited datasets suggested that TNF, SOCS3, JUN, TNFAIP3, and CXCL9 may serve as potential infection biomarkers and could deserve value as prognostic biomarkers for leishmaniasis. Additionally, inferred data from miRWalk revealed a significant degree of interaction of a number of miRNAs (hsa-miR-8085, hsa-miR-4673, hsa-miR-4743-3p, hsa-miR-892c-3p, hsa-miR-4644, hsa-miR-671-5p, hsa-miR-7106-5p, hsa-miR-4267, hsa-miR-5196-5p, and hsa-miR-4252) with the majority of the hub genes, suggesting such miRNAs play a crucial role afterwards parasite infection. The hub genes and hub miRNAs identified in this study could be potentially suggested as therapeutic targets or biomarkers for the management of leishmaniasis.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology. MATERIALS AND METHODS: We analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC. RESULTS: The PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence. CONCLUSION: Our deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Identifying hub genes in response to ustekinumab and the impact of ustekinumab treatment on fibrosis in Crohn's disease.

Introduction: Crohn's disease (CD) is a chronic inflammatory disease. Approximately 50% of patients with CD progressed from inflammation to fibrosis. Currently, there are no effective drugs for treating intestinal fibrosis. Biologic therapies for CD such as ustekinumab have benefited patients; however, up to 30% of patients with CD have no response to initial treatment, and the effect of ustekinumab on intestinal fibrosis is still uncertain. Therefore, it is of great significance to explore the predictive factors of ustekinumab treatment response and the effect of ustekinumab on intestinal fibrosis. Materials and methods: Public datasets-GSE207465 (blood samples) and GSE112366 and GSE207022 (intestinal samples)-were downloaded and analyzed individually (unmerged) based on the treatment response. Differentially expressed genes (DEGs) were identified by the "limma" R package and changes in immune cell infiltration were determined by the "CIBERSORT" R package in both blood and intestinal samples at week 0 (before treatment). To find predictive factors of ustekinumab treatment response, the weighted gene co-expression network analysis (WGCNA) R package was used to identify hub genes in GSE112366. Hub genes were then verified in GSE207022, and a prediction model was built by random forest algorithm. Furthermore, fibrosis-related gene changes were analyzed in ileal samples before and after treatment with ustekinumab. Results: (1) Our analysis found that MUC1, DUOX2, LCN2, and PDZK1IP1 were hub genes in GSE112366. GSE207022 revealed that MUC1 (AUC:0.761), LCN2 (AUC:0.79), and PDZK1IP1 (AUC:0.731) were also lower in the response group. Moreover, the random forest model was shown to have strong predictive capabilities in identifying responders (AUC = 0.875). To explore the relationship between intestinal tissue and blood, we found that ITGA4 had lower expression in the intestinal and blood samples of responders. The expression of IL18R1 is also lower in responders' intestines. IL18, the ligand of IL18R1, was also found to have lower expression in the blood samples from responders vs. non-responders. (2) GSE112366 revealed a significant decrease in fibrosis-related module genes (COL4A1, TUBB6, IFITM2, SERPING1, DRAM1, NAMPT, MMP1, ZEB2, ICAM1, PFKFB3, and ACTA2) and fibrosis-related pathways (ECM-receptor interaction and PI3K-AKT pathways) after ustekinumab treatment. Conclusion: MUC1, LCN2, and PDZK1IP1 were identified as hub genes in intestinal samples, with lower expression indicating a positive prediction of ustekinumab treatment response. Moreover, ITGA4 and IL18/IL18R1 may be involved in the treatment response in blood and intestinal samples. Finally, ustekinumab treatment was shown to significantly alter fibrotic genes and pathways.

Genetic regulators of a resource polyphenism interact to couple predatory morphology and behaviour.

Phenotypic plasticity often requires the coordinated response of multiple traits observed individually as morphological, physiological or behavioural. The integration, and hence functionality, of this response may be influenced by whether and how these component traits share a genetic basis. In the case of polyphenism, or discrete plasticity, at least part of the environmental response is categorical, offering a simple readout for determining whether and to what degree individual components of a plastic response can be decoupled. Here, we use the nematode Pristionchus pacificus, which has a resource polyphenism allowing it to be a facultative predator of other nematodes, to understand the genetic integration of polyphenism. The behavioural and morphological consequences of perturbations to the polyphenism's genetic regulatory network show that both predatory activity and ability are strongly influenced by morphology, different axes of morphological variation are associated with different aspects of predatory behaviour, and rearing environment can decouple predatory morphology from behaviour. Further, we found that interactions between some polyphenism-modifying genes synergistically affect predatory behaviour. Our results show that the component traits of an integrated polyphenic response can be decoupled and, in principle, selected upon individually, and they suggest that multiple routes to functionally comparable phenotypes are possible.

Fluoride impairs vascular smooth muscle A7R5 cell lines via disrupting amino acids metabolism.

Given the insidious and high-fatality nature of cardiovascular diseases (CVDs), the emergence of fluoride as a newly identified risk factor demands serious consideration alongside traditional risk factors. While vascular smooth muscle cells (VSMCs) play a pivotal role in the progression of CVDs, the toxicological impact of fluoride on VSMCs remains largely uncharted. In this study, we constructed fluorosis model in SD rats and A7R5 aortic smooth muscle cell lines to confirm fluoride impaired VSMCs. Fluoride aggravated the pathological damage of rat aorta in vivo. Then A7R5 were exposed to fluoride with concentration ranging from 0 to 1200 µmol/L over a 24-h period, revealing a dose-dependent inhibition of cell proliferation and migration. The further metabolomic analysis showed alterations in metabolite profiles induced by fluoride exposure, notably decreasing organic acids and lipid molecules level. Additionally, gene network analysis underscored the frequency of fluoride's interference with amino acids metabolism, potentially impacting the tricarboxylic acid (TCA) cycle. Our results also highlighted the ATP-binding cassette (ABC) transporters pathway as a central element in VSMC impairment. Moreover, we observed a dose-dependent increase in osteopontin (OPN) and α-smooth muscle actin (α-SMA) mRNA level and a dose-dependent decrease in ABC subfamily C member 1 (ABCC1) and bestrophin 1 (BEST1) mRNA level. These findings advance our understanding of fluoride as a CVD risk factor and its influence on VSMCs and metabolic pathways, warranting further investigation into this emerging risk factor.

Identification and diagnostic potential of hsa_circ_101303 in colorectal cancer: unraveling a regulatory network.

BACKGROUND: The role of novel circular RNAs (circRNAs) in colorectal cancer (CRC) remains to be determined. This study aimed to identify a novel circRNA involved in CRC pathogenesis, assess its diagnostic value, and construct a regulatory network. METHODS: Differential expression analysis was conducted using circRNA datasets to screen for differentially expressed circRNAs. The expression of selected circRNAs was validated in external datasets and clinical samples. Diagnostic value of plasma circRNA levels in CRC was assessed. A competing endogenous RNA (ceRNA) network was constructed for the circRNA using TCGA dataset. RESULTS: Analysis of datasets revealed that hsa_circ_101303 was significantly overexpressed in CRC tissues compared to normal tissues. The upregulation of hsa_circ_101303 in CRC tissues was further confirmed through the GSE138589 dataset and clinical samples. High expression of hsa_circ_101303 was associated with advanced N stage, M stage, and tumor stage in CRC. Plasma levels of hsa_circ_101303 were markedly elevated in CRC patients and exhibited moderate diagnostic ability for CRC (AUC = 0.738). The host gene of hsa_circ_101303 was also found to be related to the TNM stage of CRC. Nine miRNAs were identified as target miRNAs for hsa_circ_101303, and 27 genes were identified as targets of these miRNAs. Subsequently, a ceRNA network for hsa_circ_101303 was constructed to illustrate the interactions between the nine miRNAs and 27 genes. CONCLUSIONS: The study identifies hsa_circ_101303 as a highly expressed circRNA in CRC, which is associated with the progression of the disease. Plasma levels of hsa_circ_101303 show promising diagnostic potential for CRC. The ceRNA network for hsa_circ_101303 provides valuable insights into the regulatory mechanisms underlying CRC.

Bioinformatics analysis of signature genes related to cell death in keratoconus.

Keratoconus is corneal disease in which the progression of conical dilation of cornea leads to reduced visual acuity and even corneal perforation. However, the etiology mechanism of keratoconus is still unclear. This study aims to identify the signature genes related to cell death in keratoconus and examine the function of these genes. A dataset of keratoconus from the GEO database was analysed to identify the differentially expressed genes (DEGs). A total of 3558 DEGs were screened from GSE151631. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that they mainly involved in response to hypoxia, cell-cell adhesion, and IL-17 signaling pathway. Then, the cell death-related genes datasets were intersected with the above 3558 DEGs to obtain 70 ferroptosis-related DEGs (FDEGs), 32 autophagy-related DEGs (ADEGs), six pyroptosis-related DEGs (PDEGs), four disulfidptosis-related DEGs (DDEGs), and one cuproptosis-related DEGs (CDEGs). After using Least absolute shrinkage and selection operator (LASSO), Random Forest analysis, and receiver operating characteristic (ROC) curve analysis, one ferroptosis-related gene (TNFAIP3) and five autophagy-related genes (CDKN1A, HSPA5, MAPK8IP1, PPP1R15A, and VEGFA) were screened out. The expressions of the above six genes were significantly decreased in keratoconus and the area under the curve (AUC) values of these genes was 0.944, 0.893, 0.797, 0.726, 0.882 and 0.779 respectively. GSEA analysis showed that the above six genes mainly play an important role in allograft rejection, asthma, and circadian rhythm etc. In conclusion, the results of this study suggested that focusing on these genes and autoimmune diseases will be a beneficial perspective for the keratoconus etiology research.

Correlations reveal the hierarchical organization of biological networks with latent variables.

Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.

BACKGROUND: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia. METHODS: Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated. RESULTS: The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93). CONCLUSION: We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.