Identification and analysis of key genes in osteosarcoma using bioinformatics

Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses

BACKGROUND

Specific alterations in gut microbiota and metabolites have been linked to AMI, with CBLB potentially playing an essential role. However, the precise interactions remain understudied, creating a significant gap in our understanding. This study aims to address this by exploring these interactions in CBLB-intervened AMI mice using transcriptome sequencing, 16 S rDNA, and non-targeted metabolite analysis.

METHODS

To probe the therapeutic potential and mechanistic underpinnings of CBLB overexpression in AMI, we utilized an integrative multi-omics strategy encompassing transcriptomics, metabolomics, and 16s rDNA sequencing. We selected these particular methods as they facilitate a holistic comprehension of the intricate interplay between the host and its microbiota, and the potential effects on the host's metabolic and gene expression profiles. The uniqueness of our investigation stems from utilizing a multi-omics approach to illuminate the role of CBLB in AMI, an approach yet unreported to the best of our knowledge. Our experimental protocol encompassed transfection of CBLB lentivirus-packaged vectors into 293T cells, followed by subsequent intervention in AMI mice. Subsequently, we conducted pathological staining, fecal 16s rDNA sequencing, and serum non-targeted metabolome sequencing. We applied differential expression analysis to discern differentially expressed genes (DEGs), differential metabolites, and differential microbiota. We performed protein-protein interaction analysis to identify core genes, and conducted correlation studies to clarify the relationships amongst these core genes, paramount metabolites, and key microbiota.

RESULTS

Following the intervention of CBLB in AMI, we observed a significant decrease in inflammatory cell infiltration and collagen fiber formation in the infarcted region of mice hearts. We identified key changes in microbiota, metabolites, and DEGs that were associated with this intervention. The findings revealed that CBLB has a significant correlation with DEGs, differential metabolites and microbiota, respectively. This suggests it could play a pivotal role in the regulation of AMI.

CONCLUSION

This study confirmed the potential of differentially expressed genes, metabolites, and microbiota in AMI regulation post-CBLB intervention. Our findings lay groundwork for future exploration of CBLB's role in AMI, suggesting potential therapeutic applications and novel research directions in AMI treatment strategies.

Genome-wide analysis reveals genomic diversity and signatures of selection in Qinchuan beef cattle

BACKGROUND

Indigenous Chinese cattle have abundant genetic diversity and a long history of artificial selection, giving local breeds advantages in adaptability, forage tolerance and resistance. The detection of selective sweeps and comparative genome analysis of selected breeds and ancestral populations provide a basis for understanding differences among breeds and for the identification and utilization of candidate genes. We investigated genetic diversity, population structure, and signatures of selection using genome-wide sequencing data for a new breed of Qinchuan cattle (QNC, n = 21), ancestral Qinchuan cattle (QCC, n = 20), and Zaosheng cattle (ZSC, n = 19).

RESULTS

A population structure analysis showed that the ancestry components of QNC and ZSC were similar. In addition, the QNC and ZSC groups showed higher proportions of European taurine ancestry than that of QCC, and this may explain the larger body size of QNC, approaching that of European cattle under long-term domestication and selection. A neighbor-joining tree revealed that QCC individuals were closely related, whereas QNC formed a distinct group. To search for signatures of selection in the QNC genome, we evaluated nucleotide diversity (θπ), the fixation index (FST) and Tajima's D. Overlapping selective sweeps were enriched for one KEGG pathway, the apelin signaling pathway, and included five candidate genes (MEF2A, SMAD2, CAMK4, RPS6, and PIK3CG). We performed a comprehensive review of genomic variants in QNC, QCC, and ZSC using whole-genome sequencing data. QCC was rich in novel genetic diversity, while diversity in QNC and ZSC cattle was reduced due to strong artificial selection, with divergence from the original cattle.

CONCLUSIONS

We identified candidate genes associated with production traits. These results support the success of selective breeding and can guide further breeding and resource conservation of Qinchuan cattle.

Low FHL1 expression indicates a good prognosis and drug sensitivity in ovarian cancer

Chemotherapy resistance is the main reason for the poor prognosis of ovarian cancer (OC). FHL1 is an important tumour regulator, but its relationship with the prognosis, drug resistance, and tumour microenvironment of OC is unknown. Immunohistochemistry was used to determine FHL1 expression in OC. Kaplan‒Meier plotter was used for survival analysis. The value of gene expression in predicting drug resistance was estimated using the area under the curve (AUC). Bivariate correlation was used to determine the coexpression of two genes. Functional cluster and pathway enrichment were used to uncover hidden signalling pathways. The relationship between gene levels and the tumour microenvironment was visualised through the ggstatsplot and pheatmap packages. The mRNA and protein levels of FHL1 were downregulated in 426 and 100 OC tissues, respectively. Low FHL1 expression was correlated with good progression-free survival (PFS), postprogression survival, and overall survival (OS) in 1815 OC patients, and was further confirmed to be associated with good OS by immunohistochemistry in 152 OC tissues. Furthermore, FHL1 was downregulated in drug-sensitive tissues, while its high expression predicted drug resistance (AUC > 0.65). Mechanistically, FHL1 was coexpressed with FLNC, CAV1, PPP1R12B, and FLNA at the mRNA and protein levels in 558 and 174 OC tissues, respectively, and their expression was downregulated in OC. Additionally, very strong coexpression of FHL1 with the four genes was identified in at least 23 different tumours. Low expression of the four genes was associated with good PFS, and the combination of FHL1 with the four genes provided better prognostic power. Meanwhile, the expression of all five genes was strongly and positively associated with the abundance of macrophages. Low FHL1 expression acts as a favourable factor in OC, probably via positive coexpression with FLNC, CAV1, PPP1R12B, and FLNA.

Combined analysis of 16S rDNA sequencing and metabolomics to find biomarkers of drug-induced liver injury

Drug induced liver injury (DILI) is a kind of liver dysfunction which caused by drugs, and gut microbiota could affect liver injury. However, the relationship between gut microbiota and its metabolites in DILI patients is not clear. The total gut microbiota DNA was extracted from 28 DILI patient and 28 healthy control volunteers (HC) and 16S rDNA gene were amplified. Next, differentially metabolites were screened. Finally, the correlations between the diagnostic strains and differentially metabolites were studied.The richness and uniformity of the bacterial communities decreased in DILI patients, and the structure of gut microbiota changed obviously. Enterococcus and Veillonella which belong to Firmicutes increased in DILI, and Blautia and Ralstonia which belong to Firmicutes, Dialister which belongs to Proteobacteria increased in HC. In addition, these diagnostic OTUs of DILI were associated with the DILI damage mechanism. On the other hands, there were 66 differentially metabolites between DILI and HC samples, and these metabolites were mainly enriched in pyrimidine metabolism and steroid hormone biosynthesis pathways. Furthermore, the collinear network map of the key microbiota-metabolites were constructed and the results indicated that Cortodoxone, Prostaglandin I1, Bioyclo Prostaglandin E2 and Anacardic acid were positively correlated with Blautia and Ralstonia, and negatively correlated with Veillonella.This study analyzed the changes of DILI from the perspective of gut microbiota and metabolites. Key strains and differentially metabolites of DILI were screened and the correlations between them were studied. This study further illustrated the mechanism of DILI.

Uncovering ferroptosis in Parkinson's disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products

Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson's disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes. Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerDEGs for signature genes. We mined the interacted natural product components based on screened signature genes. Finally, we mapped a network combined with ingredients and signature genes, then carried out molecular docking validation of core ingredients and targets to uncover potential therapeutic targets and ingredients for PD. Results: We identified 109 PDFerDEGs that were significantly enriched in biological processes and KEGG pathways associated with ferroptosis (including iron ion homeostasis, iron ion transport and ferroptosis, etc.). We obtained 29 overlapping genes and identified 6 hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2) by screening with two machine learning algorithms. Based on this, we screened 263 natural product components and subsequently mapped the "Overlapping Genes-Ingredients" network. According to the network, top 5 core active ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) were molecularly docked to hub genes to reveal their potential role in the treatment of ferroptosis in PD. Conclusion: Our findings suggested that PDFerDEGs are associated with ferroptosis and play a role in the progression of PD. Taken together, core ingredients (quercetin, 17-beta-estradiol, glycerin, trans-resveratrol, and tocopherol) bind well to hub genes (TLR4, IL6, ADIPOQ, PTGS2, ATG7, and FADS2), highlighting novel biomarkers for PD.

Comparative transcriptome analysis identifies candidate genes related to seed coat color in rapeseed

Yellow seed coat in rapeseed (Brassica napus) is a desirable trait that can be targeted to improve the quality of this oilseed crop. To better understand the inheritance mechanism of the yellow-seeded trait, we performed transcriptome profiling of developing seeds in yellow- and black-seeded rapeseed with different backgrounds. The differentially expressed genes (DEGs) during seed development showed significant characteristics, these genes were mainly enriched for the Gene Ontology (GO) terms carbohydrate metabolic process, lipid metabolic process, photosynthesis, and embryo development. Moreover, 1206 and 276 DEGs, which represent candidates to be involved in seed coat color, were identified between yellow- and black-seeded rapeseed during the middle and late stages of seed development, respectively. Based on gene annotation, GO enrichment analysis, and protein-protein interaction network analysis, the downregulated DEGs were primarily enriched for the phenylpropanoid and flavonoid biosynthesis pathways. Notably, 25 transcription factors (TFs) involved in regulating flavonoid biosynthesis pathway, including known (e.g., KNAT7, NAC2, TTG2 and STK) and predicted TFs (e.g., C2H2-like, bZIP44, SHP1, and GBF6), were identified using integrated gene regulatory network (iGRN) and weight gene co-expression networks analysis (WGCNA). These candidate TF genes had differential expression profiles between yellow- and black-seeded rapeseed, suggesting they might function in seed color formation by regulating genes in the flavonoid biosynthesis pathway. Thus, our results provide in-depth insights that facilitate the exploration of candidate gene function in seed development. In addition, our data lay the foundation for revealing the roles of genes involved in the yellow-seeded trait in rapeseed.

Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment

The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets—GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.

Molecular Genomic Study of Inhibin Molecule Production through Granulosa Cell Gene Expression in Inhibin-Deficient Mice

Inhibin is a molecule that belongs to peptide hormones and is excreted through pituitary gonadotropins stimulation action on the granulosa cells of the ovaries. However, the differential regulation of inhibin and follicle-stimulating hormone (FSH) on granulosa cell tumor growth in mice inhibin-deficient females is not yet well understood. The objective of this study was to evaluate the role of inhibin and FSH on the granulosa cells of ovarian follicles at the premature antral stage. This study stimulated immature wild-type (WT) and Inhibin-α knockout (Inha−/−) female mice with human chorionic gonadotropin (hCG) and examined hCG-induced gene expression changes in granulosa cells. Also, screening of differentially expressed genes (DEGs) was performed in the two groups under study. In addition, related modules to external traits and key gene drivers were determined through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm. The results identified a number of 1074 and 931 DEGs and 343 overlapping DEGs (ODEGs) were shared in the two groups. Some 341 ODEGs had high relevance and consistent expression direction, with a significant correlation coefficient (r2 = 0.9145). Additionally, the gene co-expression network of selected 153 genes showed 122 nodes enriched to 21 GO biological processes (BP) and reproduction and 3 genes related to genomic pathways. By using principal component analysis (PCA), the 14 genes in the regulatory network were fixed and the cumulative proportion of fitted top three principal components was 94.64%. In conclusion, this study revealed the novelty of using ODEGs for investigating the inhibin and FSH hormone pathways that might open the way toward gene therapy for granulosa cell tumors. Also, these genes could be used as biomarkers for tracking the changes in inhibin and FSH hormone from the changes in the nutrition pattern.

Osteosarcoma-Specific Genes as a Diagnostic Tool and Clinical Predictor of Tumor Progression

Simple Summary

The standard method for the diagnosis and monitoring of osteosarcoma is biopsy and tumor imaging, which causes discomfort to patients and is difficult to repeat. A blood sample can be used as a non-invasive method for monitoring tumor material. Vimentin and ezrin show clinical significance in samples obtained from OS patients but need circulating tumor cell purification, since they are expressed in leukocytes. Due to the low-temperature storage of the samples, it proved impossible to perform purification to remove the contamination. We propose that novel or OS-specific biomarkers using differential gene expression from the Gene Expression Omnibus (GEO) database is a promising approach for developing diagnostic and tumor progression strategies. Seven genes from the database showed significant expression in OS cell lines/primary cells compared to a normal blood donor, together with ezrin and VIM. The expression of the five candidate genes together with ezrin and vimentin were quantified by qRT-PCR and analyzed using a mathematical model with high efficiency to discriminate between OS patients and normal samples, resulting in the selection of three candidate genes: COL5A2 (one of the five from the database) as well as ezrin and VIM. Our study demonstrates that these genes in retrospective samples could serve as tools of OS detection and predictors of disease progression.

Abstract

A liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.

Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer.

Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB).

Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status.

Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

BACKGROUND

Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment.

RESULTS

We found 610 independent prognosis-related 5'-cytosine-phosphate-guanine-3' (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients' survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166.

CONCLUSIONS

This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

Osteosarcoma is one of the most aggressive malignant bone tumors worldwide. Although great advancements have been made in its treatment owing to the advent of neoadjuvant chemotherapy, the problem of lung metastasis is a major obstacle in the improvement of survival outcomes. Thus, the aim of the present study is to screen novel and key biomarkers, which may act as potential prognostic markers and therapeutic targets in osteosarcoma. We utilized the robust rank aggregation (RRA) method to integrate three osteosarcoma microarray datasets downloaded from the Gene Expression Omnibus (GEO) database, and we identified the robust differentially expressed genes (DEGs) between primary and metastatic osteosarcoma tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of robust DEGs. The results of enrichment analysis showed that the robust DEGs were closely associated with osteosarcoma development and progression. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm, and we found that macrophages are the most principal infiltrating immune cells in osteosarcoma, especially macrophages M0 and M2. Then, the protein–protein interaction network and key modules were constructed by Cytoscape, and 10 hub genes were selected by plugin cytoHubba from the whole network. The survival analysis of hub genes was also carried out based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The integrated bioinformatics analysis was utilized to provide new insight into osteosarcoma development and metastasis and identified EGR1, CXCL10, MYC, and CXCR4 as potential biomarkers for prognosis of osteosarcoma.

Identification of Lysosome-Associated Protein Transmembrane-4 as a Novel Therapeutic Target for Osteosarcoma Treatment

OBJECTIVE

The aim of the study is to evaluate the expression of lysosome-associated protein transmembrane-4 (LAPTM4B) in human osteosarcoma tissue samples collected in our hospital, and to explore the possible correlations between the clinical pathological features of osteosarcoma patients and LAPTM4B expression.

METHODS

Immunohistochemical (IHC) assays were performed to detect the expression levels of LAPTM4B in 62 tissue samples of osteosarcoma tissues and corresponding non-tumor tissues. According to LAPTM4B staining intensity in tumor tissues, osteosarcoma patients were classified into LAPTM4B high expression and low expression groups. In addition, the potential correlations between LAPTM4B expression levels and clinical pathological features were evaluated. In addition, we detected the effects of LAPTM4B on the proliferation and invasion of esteosarcoma cells through colony formation assay and transwell assay, respectively. We further explored the potential effects of LAPTM4B on tumor growth and metastasis using in vivo animal model.

RESULTS

We revealed that LAPTM4B was highly expressed in human osteosarcoma tissues. We determined the significance between LAPTM4B and clinical features, including the tumor size (P = 0.004*) and the clinical stage (P = 0.035*) of osteosarcoma patients. Our results further demonstrated that ablation of LAPTM4B obviously blocked the proliferation and invasion of osteosarcoma cells in vitro and restrained tumor growth and metastasis in mice.

CONCLUSION

We investigated the potential involvement of LAPTM4B in osteosarcoma progression and confirmed LAPTM4B as a novel therapeutic target for osteosarcoma.

Genome-Wide Transcriptome Analysis Reveals Intermittent Fasting-Induced Metabolic Rewiring in the Liver

Scope: Intermittent fasting (IF) has been extensively reported to promote improved energy homeostasis and metabolic switching. While IF may be a plausible strategy to ameliorate the epidemiological burden of disease in many societies, our understanding of the underlying molecular mechanisms behind such effects is still lacking. The present study has sought to investigate the relationship between IF and changes in gene expression. We focused on the liver, which is highly sensitive to metabolic changes due to energy status. Mice were randomly assigned to ad libitum feeding or IF for 16 hours per day or for 24 hours on alternate days for 3 months, after which genome-wide transcriptome analysis of the liver was performed using RNA sequencing. Our findings revealed that IF caused robust transcriptomic changes in the liver that led to a complex array of metabolic changes. We also observed that the IF regimen produced distinct profiles of transcriptomic changes, highlighting the significance of temporally different periods of energy restriction. Our results suggest that IF can regulate metabolism via transcriptomic mechanisms and provide insight into how genetic interactions within the liver might lead to the numerous metabolic benefits of IF.

Identification of candidate genes and miRNAs associated with neuropathic pain induced by spared nerve injury

Neuropathic pain (NP) is a complex, chronic pain condition caused by injury or dysfunction affecting the somatosensory nervous system. This study aimed to identify crucial genes and miRNAs involved in NP. Microarray data (access number GSE91396) were downloaded from the Gene Expression Omnibus (GEO). Murine RNA-seq samples from three brain regions [nucleus accumbens, (NAc); medial prefrontal cortex, (mPFC) and periaqueductal gray, (PAG)] were compared between the spared nerve injury (SNI) model and a sham surgery. After data normalization, differentially expressed RNAs were screened using the limma package and functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery. The microRNA (miRNA/miR)-mRNA regulatory network and miRNA-target gene-pathway regulatory network were constructed using Cytoscape software. A total of 2,776 differentially expressed RNAs (219 miRNAs and 2,557 mRNAs) were identified in the SNI model compared with the sham surgery group. A total of two important modules (red and turquoise module) were found to be related to NP using weighed gene co-expression network analysis (WGCNA) for the 2,325 common differentially expressed RNAs in three brain regions. The differentially expressed genes (DEGs) in the miRNA-mRNA regulatory network were significantly enriched in 21 Gene Ontology terms and five pathways. A total of four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR-208a-5p, miR-7688-3p, miR-344f-3p, miR-135b-3p and miR-135a-2-3p) were revealed according to the miRNA-target gene-pathway regulatory network to be related to NP. Four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR-208a-5p, miR-7688-3p, miR-344f-3p, miR-135b-3p and miR-135a-2-3p) were differentially expressed in SNI, indicating their plausible roles in NP pathogenesis.

NKX2-2 Suppresses Osteosarcoma Metastasis and Proliferation by Downregulating Multiple Target Genes

Osteosarcoma is the most common primary malignant bone tumor. However, our understanding of the molecular mechanism underlying its pathogenesis is incomplete. Studies have shown that aberrant expression of NK homeobox (NKX) genes may be involved in the oncogenesis of various cancers. Here, through migration screening assay, we found that a series of NKX genes inhibit the migration of osteosarcoma cells. Among these genes, NKX2-2 is a bona fide tumor suppressor for osteosarcoma. Overexpression of NKX2-2 decreases the migration, invasion, proliferation and colony formation of osteosarcoma cells in vitro and suppresses tumor growth and metastasis in vivo. Moreover, based on the results from both in vitro and in vivo studies, the transcriptional activation domain of NKX2-2 is important for its tumor suppressor function. Mechanistically, we revealed that NKX2-2 acts as a tumor suppressor partially by mediating the transcriptional downregulation of COL5A2, PLAU, SEMA7A and S1PR1 genes. In summary, our studies of NKX2-2 revealed new molecular mechanisms underlying osteosarcoma proliferation and metastasis and may provide a series of potential therapeutic targets for osteosarcoma.

Regulatory effects of microRNA‑184 on osteosarcoma via the Wnt/β‑catenin signaling pathway

The present study aimed to investigate the role of microRNA (miRNA/miR)‑184 in osteosarcoma growth, development and metastasis, and the effects of miRNA‑184 on the proliferation, invasion and metastasis of osteosarcoma cells and associated mechanisms. In vitro, miR‑184 was transfected into U‑2OS cells and 143B cells. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was used to detect the expression of miR‑184. MTT was utilized to detect cell proliferation. A Transwell assay was applied to detect cell invasiveness. In vivo, an osteosarcoma tibial orthotopic metastatic tumor model was established, and western blotting and RT‑qPCR were used to detect the expression of Wnt and β‑catenin. Following the overexpression of miR‑184, the proliferation and cell invasion ability were significantly increased in U‑2OS and 143B cells. Following inhibition of miR‑184, cell proliferation and cell invasion ability were significantly decreased. In nude mice, tumor volume significantly increased following overexpression of miR‑184, and Wnt and phosphorylated β‑catenin levels were significantly increased. Following miR‑184 inhibition, tumor volume was significantly decreased, and Wnt and phosphorylated β‑catenin levels were significantly decreased. The results of the present study indicated that the Wnt/β‑catenin signaling pathway serves a key function in the mechanism of osteosarcoma. Inhibition of miRNA‑184 may reduce tumor volume of osteosarcoma via regulation of the Wnt/β‑catenin signaling pathway and may provide a novel strategy for the future diagnosis and treatment of osteosarcoma.